Sync docs between NeuralNetworks.h and HAL file
Test: VtsHalNeuralnetworksV1_2TargetTest
Fix: 116445845
Change-Id: I4287a947faa260f69ba50c746bd40a0981548e50
diff --git a/neuralnetworks/1.2/types.hal b/neuralnetworks/1.2/types.hal
index 2e48ba0..3eda14f 100644
--- a/neuralnetworks/1.2/types.hal
+++ b/neuralnetworks/1.2/types.hal
@@ -38,6 +38,8 @@
*
* Values of this operand type are either true or false. A zero value
* represents false; any other value represents true.
+ *
+ * Available since API level 29.
*/
BOOL = 6,
/**
@@ -48,41 +50,49 @@
* realValue = integerValue * scale.
*
* scale is a 32 bit floating point with value greater than zero.
+ *
+ * Available since API level 29.
*/
TENSOR_QUANT16_SYMM = 7,
- /** A tensor of IEEE 754 16 bit floating point values. */
+ /**
+ * A tensor of IEEE 754 16 bit floating point values.
+ *
+ * Available since API level 29.
+ */
TENSOR_FLOAT16 = 8,
/**
* A tensor of 8 bit boolean values.
*
* Values of this operand type are either true or false. A zero value
* represents false; any other value represents true.
+ *
+ * Available since API level 29.
*/
TENSOR_BOOL8 = 9,
- /** An IEEE 754 16 bit floating point scalar value. */
+ /**
+ * An IEEE 754 16 bit floating point scalar value.
+ *
+ * Available since API level 29.
+ */
FLOAT16 = 10,
/**
* A tensor of 8 bit signed integers that represent real numbers.
*
- * This tensor is associated with additional fields that are
- * used to convert the 8 bit signed integer to the real value and vice versa.
+ * This tensor is associated with additional fields that can
+ * be used to convert the 8 bit signed integer to the real value and vice versa.
* These fields are:
* - channelDim: a 32 bit unsigned integer indicating channel dimension.
* - scales: an array of positive 32 bit floating point values.
* The size of the scales array must be equal to dimensions[channelDim].
- * These fields are located inside Operand's extraParams union, inside the
- * SymmPerChannelQuantParams struct.
*
- * An Operand of this type must use the 'channelQuant' variant of its
- * extraParams field.
+ * The channel dimension of this tensor must not be unknown (dimensions[channelDim] != 0).
*
- * The channel dimension of this tensor must be known, i.e.
- * dimensions[channelDim] must be non-zero.
- *
- * The formula for real values:
+ * The formula is:
* realValue[..., C, ...] =
* integerValue[..., C, ...] * scales[C]
* where C is an index in the Channel dimension.
+ *
+ * Available since API level 29.
*/
TENSOR_QUANT8_SYMM_PER_CHANNEL = 11,
/**
@@ -95,6 +105,8 @@
*
* The formula is:
* real_value = (integer_value - zeroPoint) * scale.
+ *
+ * Available since API level 29.
*/
TENSOR_QUANT16_ASYMM = 12,
/**
@@ -105,8 +117,24 @@
* realValue = integerValue * scale.
*
* scale is a 32 bit floating point with value greater than zero.
+ *
+ * Available since API level 29.
*/
TENSOR_QUANT8_SYMM = 13,
+ /*
+ * DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
+ * OEM operation and data types.
+ *
+ * OEM specific scalar value.
+ * OEM = 10000,
+ */
+ /*
+ * DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
+ * OEM operation and data types.
+ *
+ * A tensor of OEM specific values.
+ * TENSOR_OEM_BYTE = 10001,
+ */
/* ADDING A NEW FUNDAMENTAL TYPE REQUIRES UPDATING THE VALUE OF
* OperandTypeRange::FUNDAMENTAL_MAX.
*/
@@ -132,64 +160,4295 @@
*
* The type of an operation in a model.
*/
-enum OperationType : @1.1::OperationType {
- // TODO(b/116445845): Sync docs when all ops are implemented.
+enum OperationType : int32_t {
+ /**
+ * Adds two tensors, element-wise.
+ *
+ * Takes two input tensors of identical {@link OperandType} and compatible
+ * dimensions. The output is the sum of both input tensors, optionally
+ * modified by an activation function.
+ *
+ * Two dimensions are compatible when:
+ * 1. they are equal, or
+ * 2. one of them is 1
+ *
+ * The size of the output is the maximum size along each dimension of the
+ * input operands. It starts with the trailing dimensions, and works its
+ * way forward.
+ *
+ * Example:
+ *
+ * input1.dimension = {4, 1, 2}
+ * input2.dimension = {5, 4, 3, 1}
+ * output.dimension = {5, 4, 3, 2}
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
+ * as input0.
+ * * 2: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ *
+ * Outputs:
+ * * 0: The sum, a tensor of the same {@link OperandType} as input0.
+ *
+ * Available since API level 27.
+ */
+ ADD = @1.1::OperationType:ADD,
+
+ /**
+ * Performs a 2-D average pooling operation.
+ *
+ * The output dimensions are functions of the filter dimensions, stride, and
+ * padding.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[b, i, j, channel] =
+ * sum_{di, dj}(
+ * input[b, strides[1] * i + di, strides[2] * j + dj, channel]
+ * ) / sum(1)
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ *
+ * Both explicit padding and implicit padding are supported.
+ *
+ * Inputs (explicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * the input.
+ * * 1: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the left, in the ‘width’ dimension.
+ * * 2: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the right, in the ‘width’ dimension.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the top, in the ‘height’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the bottom, in the ‘height’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the filter
+ * width.
+ * * 8: An {@link OperandType::INT32} scalar, specifying the filter
+ * height.
+ * * 9: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 10: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * Available since API level 29.
+ *
+ * Inputs (implicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * the input.
+ * * 1: An {@link OperandType::INT32} scalar, specifying the implicit
+ * padding scheme, has to be one of the
+ * following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+ * * 2: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the filter
+ * width.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the filter
+ * height.
+ * * 6: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 7: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * Available since API level 29.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape
+ * [batches, out_height, out_width, depth].
+ *
+ * Available since API level 27.
+ */
+ AVERAGE_POOL_2D = @1.1::OperationType:AVERAGE_POOL_2D,
+
+ /**
+ * Concatenates the input tensors along the given dimension.
+ *
+ * The input tensors must have identical {@link OperandType} and the same
+ * dimensions except the dimension along the concatenation axis.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM} (full support since API
+ * level 29, see the input section)
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0 ~ n-1: The list of n input tensors, of shape
+ * [D0, D1, ..., Daxis(i), ..., Dm].
+ * Before API level 29, all input tensors of
+ * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * must have the same scale and zeroPoint as the output tensor.
+ * * n: An {@link OperandType::INT32} scalar, specifying the
+ * concatenation axis.
+ *
+ * Outputs:
+ * * 0: The output, a tensor of the same {@link OperandType} as the input
+ * tensors. The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
+ *
+ * Available since API level 27.
+ */
+ CONCATENATION = @1.1::OperationType:CONCATENATION,
+
+ /**
+ * Performs an 2-D convolution operation.
+ *
+ * The CONV_2D op sweeps a 2-D filter that can mix channels together over a
+ * batch of images, applying the filter to each window of each image of the
+ * appropriate size.
+ *
+ * The output dimensions are functions of the filter dimensions, stride, and
+ * padding.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[b, i, j, channel] =
+ * sum_{di, dj, k} (
+ * input[b, strides[1] * i + di, strides[2] * j + dj, k] *
+ * filter[channel, di, dj, k]
+ * ) + bias[channel]
+ *
+ * Supported tensor {@link OperandType} configurations:
+ * * 32 bit Floating point :
+ * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
+ *
+ * * Quantized:
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output.
+ * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
+ * * * input.scale * filter.scale).
+ *
+ * Available since API level 29:
+ * * Quantized with symetric per channel quantization for the filter:
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, and output.
+ * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
+ * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
+ * * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
+ *
+ * * 16 bit Floating point:
+ * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ *
+ * Both explicit padding and implicit padding are supported.
+ *
+ * Inputs (explicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+ * specifying the input.
+ * * 1: A 4-D tensor, of shape
+ * [depth_out, filter_height, filter_width, depth_in], specifying the
+ * filter. For tensor of type
+ * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
+ * dimension (extraParams.channelQuant.channelDim) must be set to 0.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+ * tensor of type {@link OperandType::TENSOR_FLOAT32} or
+ * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same
+ * type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
+ * of 0 and bias_scale == input_scale * filter_scale. For filter tensor
+ * of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
+ * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
+ * 0 and bias_scale of 0. The actual scale of each value 'i' is equal to
+ * bias_scale[i] = input_scale * filter_scale[i].
+ * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the left, in the ‘width’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the right, in the ‘width’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the top, in the ‘height’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the bottom, in the ‘height’ dimension.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 8: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 9: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 10: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * Available since API level 29.
+ * * 11: An optional {@link OperandType::INT32} scalar, specifying the dilation
+ * factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
+ * cells between each filter element on width dimension. If this input is set,
+ * input 12 (dilation factor for height) must be specified as well.
+ * Available since API level 29.
+ * * 12: An optional {@link OperandType::INT32} scalar, specifying the dilation
+ * factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
+ * cells between each filter element on height dimension. If this input is set,
+ * input 11 (dilation factor for width) must be specified as well.
+ * Available since API level 29.
+ *
+ * Inputs (implicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+ * specifying the input.
+ * * 1: A 4-D tensor, of shape
+ * [depth_out, filter_height, filter_width, depth_in], specifying the
+ * filter. For tensor of type
+ * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
+ * dimension (extraParams.channelQuant.channelDim) must be set to 0.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+ * tensor of type {@link OperandType::TENSOR_FLOAT32} or
+ * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same
+ * type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
+ * of 0 and bias_scale == input_scale * filter_scale. For filter tensor
+ * of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
+ * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
+ * 0 and bias_scale of 0. The actual scale of each value 'i' is equal to
+ * bias_scale[i] = input_scale * filter_scale[i].
+ * * 3: An {@link OperandType::INT32} scalar, specifying the implicit
+ * padding scheme, has to be one of the
+ * following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 7: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * Available since API level 29.
+ * * 8: An optional {@link OperandType::INT32} scalar, specifying the dilation
+ * factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
+ * cells between each filter element on width dimension. If this input is set,
+ * input 9 (dilation factor for height) must be specified as well.
+ * Available since API level 29.
+ * * 9: An optional {@link OperandType::INT32} scalar, specifying the dilation
+ * factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
+ * cells between each filter element on height dimension. If this input is set,
+ * input 8 (dilation factor for width) must be specified as well.
+ * Available since API level 29.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape
+ * [batches, out_height, out_width, depth_out]. For output tensor of
+ * {@link OperandType::TENSOR_QUANT8_ASYMM}, the following condition
+ * must be satisfied: output_scale > input_scale * filter_scale (for
+ * filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
+ * this condition must be true for all filter scales).
+ *
+ * Available since API level 27.
+ */
+ CONV_2D = @1.1::OperationType:CONV_2D,
+
+ /**
+ * Performs a depthwise 2-D convolution operation.
+ *
+ * Given an input tensor of shape [batches, height, width, depth_in] and a
+ * filter tensor of shape [1, filter_height, filter_width, depth_out]
+ * containing depth_out convolutional filters of depth 1, DEPTHWISE_CONV
+ * applies a different filter to each input channel (expanding from 1
+ * channel to channel_multiplier channels for each), then concatenates the
+ * results together.
+ *
+ * The output has depth_out = depth_in * depth_multiplier channels.
+ * The output dimensions are functions of the filter dimensions, stride, and
+ * padding.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[b, i, j, k * channel_multiplier + q] =
+ * sum_{di, dj} (
+ * input[b, strides[1] * i + di, strides[2] * j + dj, k] *
+ * filter[1, di, dj, k * channel_multiplier + q]
+ * ) + bias[k * channel_multiplier + q]
+ *
+ * Supported tensor {@link OperandType} configurations:
+ * * 32 bit Floating point :
+ * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
+ *
+ * * Quantized:
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output.
+ * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
+ * * * input.scale * filter.scale).
+ *
+ * Available since API level 29:
+ * * Quantized with symetric per channel quantization for the filter:
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, and output.
+ * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
+ * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
+ * * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ *
+ * Both explicit padding and implicit padding are supported.
+ *
+ * Inputs (explicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+ * specifying the input.
+ * * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
+ * specifying the filter. For tensor of type
+ * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
+ * dimension (extraParams.channelQuant.channelDim) must be set to 3.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+ * tensor of type {@link OperandType::TENSOR_FLOAT32} or
+ * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same
+ * type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
+ * of 0 and bias_scale == input_scale * filter_scale. For filter tensor
+ * of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
+ * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
+ * 0 and bias_scale of 0. The actual scale of each value 'i' is equal to
+ * bias_scale[i] = input_scale * filter_scale[i].
+ * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the left, in the ‘width’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the right, in the ‘width’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the top, in the ‘height’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the bottom, in the ‘height’ dimension.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 8: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 9: An {@link OperandType::INT32} scalar, specifying the depthwise
+ * multiplier.
+ * * 10: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 11: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * Available since API level 29.
+ * * 12: An optional {@link OperandType::INT32} scalar, specifying the dilation
+ * factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
+ * cells between each filter element on width dimension. If this input is set,
+ * input 13 (dilation factor for height) must be specified as well.
+ * Available since API level 29.
+ * * 13: An optional {@link OperandType::INT32} scalar, specifying the dilation
+ * factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
+ * cells between each filter element on height dimension. If this input is set,
+ * input 12 (dilation factor for width) must be specified as well.
+ * Available since API level 29.
+ *
+ * Inputs (implicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+ * specifying the input.
+ * * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
+ * specifying the filter.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+ * tensor of type {@link OperandType::TENSOR_FLOAT32} or
+ * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same
+ * type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
+ * of 0 and bias_scale == input_scale * filter_scale. For filter tensor
+ * of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
+ * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
+ * 0 and bias_scale of 0. The actual scale of each value 'i' is equal to
+ * bias_scale[i] = input_scale * filter_scale[i].
+ * * 3: An {@link OperandType::INT32} scalar, specifying the implicit
+ * padding scheme, has to be one of the
+ * following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the depthwise
+ * multiplier.
+ * * 7: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 8: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * Available since API level 29.
+ * * 9: An optional {@link OperandType::INT32} scalar, specifying the dilation
+ * factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
+ * cells between each filter element on width dimension. If this input is set,
+ * input 10 (dilation factor for height) must be specified as well.
+ * Available since API level 29.
+ * * 10: An optional {@link OperandType::INT32} scalar, specifying the dilation
+ * factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
+ * cells between each filter element on height dimension. If this input is set,
+ * input 9 (dilation factor for width) must be specified as well.
+ * Available since API level 29.
+
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape
+ * [batches, out_height, out_width, depth_out]. For output tensor of
+ * {@link OperandType::TENSOR_QUANT8_ASYMM}, the following condition
+ * must be satisfied: output_scale > input_scale * filter_scale (for
+ * filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
+ * this condition must be true for all filter scales).
+ *
+ * Available since API level 27.
+ */
+ DEPTHWISE_CONV_2D = @1.1::OperationType:DEPTHWISE_CONV_2D,
+
+ /**
+ * Rearranges data from depth into blocks of spatial data.
+ *
+ * More specifically, this op outputs a copy of the input tensor where
+ * values from the depth dimension are moved in spatial blocks to the height
+ * and width dimensions. The value block_size indicates the input block size
+ * and how the data is moved.
+ *
+ * Chunks of data of size block_size * block_size from depth are rearranged
+ * into non-overlapping blocks of size block_size x block_size.
+ *
+ * The width of the output tensor is input_depth * block_size, whereas the
+ * height is input_height * block_size. The depth of the input tensor must
+ * be divisible by block_size * block_size
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ *
+ * Inputs:
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+ * specifying the input.
+ * * 1: An {@link OperandType::INT32} scalar, specifying the block_size.
+ * block_size must be >=1 and block_size * block_size must be a divisor
+ * of the input depth.
+ * * 2: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * Available since API level 29.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape [batch, height*block_size,
+ * width*block_size, depth/(block_size*block_size)].
+ *
+ * Available since API level 27.
+ */
+ DEPTH_TO_SPACE = @1.1::OperationType:DEPTH_TO_SPACE,
+
+ /**
+ * Dequantizes the input tensor.
+ *
+ * The formula is:
+ *
+ * output = (input - zeroPoint) * scale.
+ *
+ * Supported input tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_SYMM} (since API level 29)
+ * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} (since API level 29)
+ *
+ * Supported output tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}.
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: A tensor with the same shape as input0.
+ *
+ * Available since API level 27.
+ */
+ DEQUANTIZE = @1.1::OperationType:DEQUANTIZE,
+
+ /**
+ * Looks up sub-tensors in the input tensor.
+ *
+ * This operator takes for input a tensor of values (Values) and
+ * a one-dimensional tensor of selection indices (Lookups).
+ * The output tensor is the concatenation of sub-tensors of Values as
+ * selected by Lookups.
+ *
+ * Think of Values as being sliced along its first dimension:
+ * The entries in Lookups select which slices are concatenated together
+ * to create the output tensor.
+ *
+ * For example, if Values has shape of [40, 200, 300] and
+ * Lookups has shape of [3], all three values found in Lookups are
+ * expected to be between 0 and 39. The resulting tensor must
+ * have shape of [3, 200, 300].
+ *
+ * If a value in Lookups is out of bounds, the operation must fail
+ * and an error must be reported.
+ *
+ * Supported value tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported value tensor rank: from 2
+ *
+ * Inputs:
+ * * 0: Lookups. A 1-D tensor of {@link OperandType::TENSOR_INT32}.
+ * The values are indices into the first dimension of Values.
+ * * 1: Values. An n-D tensor, where n >= 2, from which sub-tensors are
+ * extracted.
+ *
+ * Output:
+ * * 0: A n-D tensor with the same rank and shape as the Values
+ * tensor, except for the first dimension which has the same size
+ * as Lookups' only dimension.
+ *
+ * Available since API level 27.
+ */
+ EMBEDDING_LOOKUP = @1.1::OperationType:EMBEDDING_LOOKUP,
+
+ /**
+ * Computes element-wise floor() on the input tensor.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor, of the same {@link OperandType} and dimensions as
+ * the input tensor.
+ *
+ * Available since API level 27.
+ */
+ FLOOR = @1.1::OperationType:FLOOR,
+
+ /**
+ * Denotes a fully (densely) connected layer, which connects all elements
+ * in the input tensor with each element in the output tensor.
+ *
+ * This layer implements the operation:
+ *
+ * outputs = activation(inputs * weights’ + bias)
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor of at least rank 2, specifying the input. If rank is
+ * greater than 2, then it gets flattened to a 2-D Tensor. The
+ * (flattened) 2-D Tensor is reshaped (if necessary) to
+ * [batch_size, input_size], where "input_size" corresponds to the
+ * number of inputs to the layer, matching the second dimension of
+ * weights, and "batch_size" is calculated by dividing the number of
+ * elements by "input_size".
+ * * 1: A 2-D tensor, specifying the weights, of shape
+ * [num_units, input_size], where "num_units" corresponds to the number
+ * of output nodes.
+ * * 2: A 1-D tensor, of shape [num_units], specifying the bias. For input
+ * tensor of {@link OperandType::TENSOR_FLOAT32}, the bias should
+ * also be of {@link OperandType::TENSOR_FLOAT32}. For input tensor
+ * of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be
+ * of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
+ * bias_scale == input_scale * filter_scale.
+ * * 3: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ *
+ * Outputs:
+ * * 0: The output tensor, of shape [batch_size, num_units]. For output
+ * tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the following
+ * condition must be satisfied:
+ * output_scale > input_scale * filter_scale.
+ *
+ * Available since API level 27.
+ */
+ FULLY_CONNECTED = @1.1::OperationType:FULLY_CONNECTED,
+
+ /**
+ * Looks up sub-tensors in the input tensor using a key-value map.
+ *
+ * This operator takes for input a tensor of values (Values),
+ * a one-dimensional tensor of selection values (Lookups) and
+ * a one-dimensional tensor that maps these values to Values
+ * indexes. The output tensor is the concatenation of sub-tensors of
+ * Values as selected by Lookups via Keys.
+ *
+ * Think of Values as being sliced along its outer-most dimension.
+ * The output is a concatenation of selected slices, with one slice
+ * for each entry of Lookups. The slice selected is the one at the
+ * same index as the Maps entry that matches the value in Lookups.
+ *
+ * For a hit, the corresponding sub-tensor of Values is included
+ * in the Output tensor. For a miss, the corresponding sub-tensor in
+ * Output must have zero values.
+ *
+ * For example, if Values has shape of [40, 200, 300],
+ * Keys should have a shape of [40]. If Lookups tensor has shape
+ * of [3], three slices are being concatenated, so the resulting tensor
+ * must have the shape of [3, 200, 300]. If the first entry in Lookups
+ * has the value 123456, that value must be located in Keys tensor.
+ * If the sixth entry of Keys contains 123456, the sixth slice of Values
+ * must be selected. If no entry in Keys has 123456, a slice of zeroes
+ * must be concatenated.
+ *
+ * Supported value tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported value tensor rank: from 2
+ *
+ * Inputs:
+ * * 0: Lookups. A 1-D {@link OperandType::TENSOR_INT32} tensor with
+ * shape [ k ].
+ * * 1: Keys. A 1-D {@link OperandType::TENSOR_INT32} tensor with shape
+ * [ n ]; Keys and Values pair represent a map, i.e., the ith element
+ * in Keys (Keys[i]) is the key to select the ith sub-tensor in Values
+ * (Values[i]), where 0 <= i <= n-1. Keys tensor *MUST* be sorted in
+ * ascending order.
+ * * 2: Values. A tensor with shape of [ n, … ]; i.e., the first dimension
+ * must be n.
+ *
+ * Outputs:
+ * * 0: Output. A tensor with shape [ k …].
+ * * 1: Hits. A boolean tensor with shape [ k ] indicates whether the lookup
+ * hits (True) or not (False).
+ * Stored as {@link OperandType::TENSOR_QUANT8_ASYMM} with offset 0
+ * and scale 1.0f.
+ * A non-zero byte represents True, a hit. A zero indicates otherwise.
+ *
+ * Available since API level 27.
+ */
+ HASHTABLE_LOOKUP = @1.1::OperationType:HASHTABLE_LOOKUP,
+
+ /**
+ * Applies L2 normalization along the depth dimension.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[batch, row, col, channel] =
+ * input[batch, row, col, channel] /
+ * sqrt(sum_{c} pow(input[batch, row, col, c], 2))
+ *
+ * For input tensor with rank less than 4, independently normalizes each
+ * 1-D slice along dimension dim.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: up to 4
+ * Tensors with rank less than 4 are only supported since API level 29.
+ *
+ * Inputs:
+ * * 0: An n-D tensor, specifying the tensor to be normalized.
+ * * 1: An optional {@link OperandType::INT32} scalar, default to -1,
+ * specifying the dimension normalization would be performed on.
+ * Negative index is used to specify axis from the end (e.g. -1 for
+ * the last axis). Must be in the range [-n, n).
+ * Available since API level 29.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} and same shape as input0.
+ *
+ * Available since API level 27.
+ */
+ L2_NORMALIZATION = @1.1::OperationType:L2_NORMALIZATION,
+
+ /**
+ * Performs an 2-D L2 pooling operation.
+ *
+ * The output dimensions are functions of the filter dimensions, stride, and
+ * padding.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[b, i, j, c] =
+ * sqrt(sum_{di, dj} pow(input[b, strides[1] * i + di, strides[2] * j + dj, c], 2) /
+ * sum(1))
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ *
+ * Both explicit padding and implicit padding are supported.
+ *
+ * Inputs (explicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * the input.
+ * * 1: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the left, in the ‘width’ dimension.
+ * * 2: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the right, in the ‘width’ dimension.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the top, in the ‘height’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the bottom, in the ‘height’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the filter
+ * width.
+ * * 8: An {@link OperandType::INT32} scalar, specifying the filter
+ * height.
+ * * 9: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 10: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * Available since API level 29.
+ *
+ * Inputs (implicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * the input.
+ * * 1: An {@link OperandType::INT32} scalar, specifying the implicit
+ * padding scheme, has to be one of the
+ * following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+ * * 2: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the filter
+ * width.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the filter
+ * height.
+ * * 6: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 7: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * Available since API level 29.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape
+ * [batches, out_height, out_width, depth].
+ *
+ * Available since API level 27.
+ */
+ L2_POOL_2D = @1.1::OperationType:L2_POOL_2D,
+
+ /**
+ * Applies Local Response Normalization along the depth dimension.
+ *
+ * The 4-D input tensor is treated as a 3-D array of 1-D vectors (along the
+ * last dimension), and each vector is normalized independently. Within a
+ * given vector, each component is divided by the weighted, squared sum of
+ * inputs within depth_radius.
+ *
+ * The output is calculated using this formula:
+ *
+ * sqr_sum[a, b, c, d] = sum(
+ * pow(input[a, b, c, d - depth_radius : d + depth_radius + 1], 2))
+ * output = input / pow((bias + alpha * sqr_sum), beta)
+ *
+ * For input tensor with rank less than 4, independently normalizes each
+ * 1-D slice along specified dimension.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: up to 4
+ * Tensors with rank less than 4 are only supported since API level 29.
+ *
+ * Inputs:
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * the input.
+ * * 1: An {@link OperandType::INT32} scalar, specifying the radius of
+ * the normalization window.
+ * * 2: An {@link OperandType::FLOAT32} scalar, specifying the bias, must
+ * not be zero.
+ * * 3: An {@link OperandType::FLOAT32} scalar, specifying the scale
+ * factor, alpha.
+ * * 4: An {@link OperandType::FLOAT32} scalar, specifying the exponent,
+ * beta.
+ * * 5: An optional {@link OperandType::INT32} scalar, default to -1,
+ * specifying the dimension normalization would be performed on.
+ * Negative index is used to specify axis from the end (e.g. -1 for
+ * the last axis). Must be in the range [-n, n).
+ * Available since API level 29.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ *
+ * Available since API level 27.
+ */
+ LOCAL_RESPONSE_NORMALIZATION = @1.1::OperationType:LOCAL_RESPONSE_NORMALIZATION,
+
+ /**
+ * Computes sigmoid activation on the input tensor element-wise.
+ *
+ * The output is calculated using this formula:
+ *
+ * output = 1 / (1 + exp(-input))
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ * For {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the scale must be 1.f / 256 and the zeroPoint must be 0.
+ *
+ * Available since API level 27.
+ */
+ LOGISTIC = @1.1::OperationType:LOGISTIC,
+
+ /**
+ * Projects an input to a bit vector via locality senstive hashing.
+ *
+ * Supported input tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported input tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: Hash functions. Dim.size == 2, DataType: Float.
+ * Tensor[0].Dim[0]: Number of hash functions.
+ * Tensor[0].Dim[1]: Number of projected output bits generated by each
+ * hash function.
+ * If the projection type is Sparse:
+ * Tensor[0].Dim[1] + ceil(log2(Tensor[0].Dim[0])) <= 32
+ *
+ * * 1: Input. Dim.size >= 1, no restriction on DataType.
+ * * 2: Weight. Optional. Dim.size == 1, DataType: Float.
+ * If not set, each input element is considered to have the same weight
+ * of 1.0.
+ * Tensor[1].Dim[0] == Tensor[2].Dim[0]
+ * * 3: Type:
+ * Sparse:
+ * Value LSHProjectionType_SPARSE(=3) (since API level 29).
+ * Computed bit vector is considered to be sparse.
+ * Each output element is an int32 made up of multiple bits
+ * computed from hash functions.
+ *
+ * NOTE: To avoid collisions across hash functions, an offset value
+ * of k * (1 << Tensor[0].Dim[1]) will be added to each signature,
+ * where k is the index of the hash function.
+ *
+ * Value LSHProjectionType_SPARSE_DEPRECATED(=1).
+ * Legacy behavior that does not include the offset value.
+ *
+ * Dense:
+ * Value LSHProjectionType_DENSE(=2).
+ * Computed bit vector is considered to be dense. Each output
+ * element represents a bit and can take the value of either
+ * 0 or 1.
+ *
+ * Outputs:
+ * * 0: If the projection type is Sparse:
+ * Output.Dim == { Tensor[0].Dim[0] }
+ * A tensor of int32 that represents hash signatures,
+ *
+ * If the projection type is Dense:
+ * Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] }
+ * A flattened tensor that represents projected bit vectors.
+ *
+ * Available since API level 27.
+ * The offset value for sparse projections was added in API level 29.
+ */
+ LSH_PROJECTION = @1.1::OperationType:LSH_PROJECTION,
+
+ /**
+ * Performs a single time step in a Long Short-Term Memory (LSTM) layer
+ *
+ * The LSTM operation is described by the following equations.
+ *
+ * \f{eqnarray*}{
+ * i_t =& \sigma(W_{xi}x_t+W_{hi}h_{t-1}+W_{ci}C_{t-1}+b_i) & \\
+ * f_t =& \sigma(W_{xf}x_t+W_{hf}h_{t-1}+W_{cf}C_{t-1}+b_f) & \\
+ * C_t =& clip(f_t \odot C_{t-1} + i_t \odot
+ * g(W_{xc}x_t+W_{hc}h_{t-1}+b_c),\ t_{cell}) & \\
+ * o_t =& \sigma(W_{xo}x_t+W_{ho}h_{t-1}+W_{co}C_t+b_o) & \\
+ * & & \\
+ * & clip(W_{proj}(o_t \odot g(C_t))+b_{proj},\ t_{proj})
+ * & if\ there\ is\ a\ projection; \\
+ * h_t =& & \\
+ * & o_t \odot g(C_t) & otherwise. \\
+ * \f}
+ * Where:
+ * * \f$x_t\f$ is the input,
+ * * \f$i_t\f$ is the input gate,
+ * * \f$f_t\f$ is the forget gate,
+ * * \f$C_t\f$ is the cell state,
+ * * \f$o_t\f$ is the output,
+ * * \f$h_t\f$ is the output state,
+ * * \f$\sigma\f$ is the logistic sigmoid function,
+ * * \f$g\f$ is the cell input and cell output activation function, usually
+ * \f$tahn\f$,
+ * * \f$W_{xi}\f$ is the input-to-input weight matrix,
+ * * \f$W_{hi}\f$ is the recurrent to input weight matrix,
+ * * \f$W_{ci}\f$ is the cell-to-input weight matrix,
+ * * \f$b_i\f$ is the input gate bias,
+ * * \f$W_{xf}\f$ is the input-to-forget weight matrix,
+ * * \f$W_{hf}\f$ is the recurrent-to-forget weight matrix,
+ * * \f$W_{cf}\f$ is the cell-to-forget weight matrix,
+ * * \f$b_f\f$ is the forget gate bias,
+ * * \f$W_{xc}\f$ is the input-to-cell weight matrix,
+ * * \f$W_{hc}\f$ is the recurrent-to-cell weight matrix,
+ * * \f$b_c\f$ is the cell bias,
+ * * \f$W_{xo}\f$ is the input-to-output weight matrix,
+ * * \f$W_{ho}\f$ is the recurrent-to-output weight matrix,
+ * * \f$W_{co}\f$ is the cell-to-output weight matrix,
+ * * \f$b_o\f$ is the output gate bias,
+ * * \f$W_{proj}\f$ is the projection weight matrix,
+ * * \f$b_{proj}\f$ is the projection bias,
+ * * \f$t_{cell}\f$ is the threshold for clipping the cell state, and
+ * * \f$t_{proj}\f$ is the threshold for clipping the projected output.
+ * * \f$\odot\f$ is the
+ * <a href="https://en.wikipedia.org/wiki/Hadamard_product_(matrices)">
+ * Hadamard product</a> that takes two matrices and produces another
+ * matrix, each element of which is the product of the corresponding
+ * elements of the input matrices.
+ *
+ * Since API level 29 LSTM supports layer normalization.
+ * In case layer normalization is used, the inputs to internal activation
+ * functions (sigmoid and \f$g\f$) are normalized, rescaled and recentered
+ * following an approach from section 3.1 from
+ * https://arxiv.org/pdf/1607.06450.pdf
+ *
+ * The operation has the following independently optional inputs:
+ * * The input-to-input weights (\f$W_{xi}\f$), recurrent-to-input weights
+ * (\f$W_{hi}\f$), cell-to-input (\f$W_{ci}\f$) weights, and input gate
+ * bias (\f$b_i\f$) either all have values, or none of them have values
+ * (i.e., all set to null). If they have no values, coupling of input and
+ * forget gates (CIFG) is used, in which case the input gate (\f$i_t\f$)
+ * is calculated using the following equation instead.
+ * \f{eqnarray*}{
+ * i_t = 1 - f_t
+ * \f}
+ * * The cell-to-forget weights (\f$W_{cf}\f$) and cell-to-output weights
+ * (\f$W_{co}\f$) either both have values or neither of them have values.
+ * If they have values, the peephole optimization is used. Additionally,
+ * if CIFG is not used, cell-to-input weights (\f$W_{ci}\f$) is also
+ * required to have values for peephole optimization.
+ * * The projection weights (\f$W_{proj}\f$) is required only for the
+ * recurrent projection layer, and should otherwise have no value.
+ * * The projection bias (\f$b_{proj}\f$) may (but not required to) have a
+ * value if the recurrent projection layer exists, and should otherwise
+ * have no value.
+ * * (API level >= 29) The four layer normalization weights either all have
+ * values or none of them have values. Layer normalization is used when
+ * values are present.
+ *
+ * References:
+ *
+ * The default non-peephole non-CIFG implementation is based on:
+ * http://www.bioinf.jku.at/publications/older/2604.pdf
+ * S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural
+ * Computation, 9(8):1735-1780, 1997.
+ *
+ * The peephole implementation and projection layer is based on:
+ * https://research.google.com/pubs/archive/43905.pdf
+ * Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory
+ * recurrent neural network architectures for large scale acoustic
+ * modeling." INTERSPEECH, 2014.
+ * (However, the concept of peephole optimization was introduced in work
+ * prior to this paper.)
+ *
+ * The coupling of input and forget gate (CIFG) is based on:
+ * http://arxiv.org/pdf/1503.04069.pdf
+ * Greff et al. "LSTM: A Search Space Odyssey"
+ *
+ * The layer normalization is based on:
+ * https://arxiv.org/pdf/1607.06450.pdf
+ * Jimmy Ba et al. "Layer Normalization"
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * All input and output tensors must be of the same type.
+ *
+ * Inputs:
+ * * 0: The input (\f$x_t\f$).
+ * A 2-D tensor of shape [batch_size, input_size], where “batch_size”
+ * corresponds to the batching dimension, and “input_size” is the size
+ * of the input.
+ * * 1: The input-to-input weights (\f$W_{xi}\f$). Optional.
+ * A 2-D tensor of shape [num_units, input_size], where “num_units”
+ * corresponds to the number of cell units.
+ * * 2: The input-to-forget weights (\f$W_{xf}\f$).
+ * A 2-D tensor of shape [num_units, input_size].
+ * * 3: The input-to-cell weights (\f$W_{xc}\f$).
+ * A 2-D tensor of shape [num_units, input_size].
+ * * 4: The input-to-output weights (\f$W_{xo}\f$).
+ * A 2-D tensor of shape [num_units, input_size].
+ * * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional.
+ * A 2-D tensor of shape [num_units, output_size], where “output_size”
+ * corresponds to either the number of cell units (i.e., “num_units”),
+ * or the second dimension of the “projection_weights”, if defined.
+ * * 6: The recurrent-to-forget weights (\f$W_{hf}\f$).
+ * A 2-D tensor of shape [num_units, output_size].
+ * * 7: The recurrent-to-cell weights (\f$W_{hc}\f$).
+ * A 2-D tensor of shape [num_units, output_size].
+ * * 8: The recurrent-to-output weights (\f$W_{ho}\f$).
+ * A 2-D tensor of shape [num_units, output_size].
+ * * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 11:The cell-to-output weights (\f$W_{co}\f$). Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 12:The input gate bias (\f$b_i\f$). Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 13:The forget gate bias (\f$b_f\f$).
+ * A 1-D tensor of shape [num_units].
+ * * 14:The cell bias (\f$b_c\f$).
+ * A 1-D tensor of shape [num_units].
+ * * 15:The output gate bias (\f$b_o\f$).
+ * A 1-D tensor of shape [num_units].
+ * * 16:The projection weights (\f$W_{proj}\f$). Optional.
+ * A 2-D tensor of shape [output_size, num_units].
+ * * 17:The projection bias (\f$b_{proj}\f$). Optional.
+ * A 1-D tensor of shape [output_size].
+ * * 18:The output state (in) (\f$h_{t-1}\f$).
+ * A 2-D tensor of shape [batch_size, output_size].
+ * * 19:The cell state (in) (\f$C_{t-1}\f$).
+ * A 2-D tensor of shape [batch_size, num_units].
+ * * 20:The activation function (\f$g\f$).
+ * A value indicating the activation function:
+ * <ul>
+ * <li>0: None;
+ * <li>1: Relu;
+ * <li>3: Relu6;
+ * <li>4: Tanh;
+ * <li>6: Sigmoid.
+ * </ul>
+ * * 21:The clipping threshold (\f$t_{cell}\f$) for the cell state, such
+ * that values are bound within [-cell_clip, cell_clip]. If set to 0.0
+ * then clipping is disabled.
+ * Until API level 29 this scalar must be of type {@link
+ * FLOAT32}. Since API level 29, if all the input
+ * tensors have type {@link OperandType::TENSOR_FLOAT32}, this
+ * scalar must be of the type {@link OperandType::FLOAT32},
+ * otherwise if all the input tensors have the type {@link
+ * TENSOR_FLOAT16}, this scalar must be of type {@link
+ * FLOAT16}.
+ * * 22:The clipping threshold (\f$t_{proj}\f$) for the output from the
+ * projection layer, such that values are bound within
+ * [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
+ * Until API level 29 this scalar must be of type {@link
+ * FLOAT32}. Since API level 29, if all the input
+ * tensors have type {@link OperandType::TENSOR_FLOAT32}, this
+ * scalar must be of the type {@link OperandType::FLOAT32},
+ * otherwise if all the input tensors have the type {@link
+ * TENSOR_FLOAT16}, this scalar must be of type {@link
+ * FLOAT16}.
+ * Since API level 29 there are additional inputs to this op:
+ * * 23:The input layer normalization weights.
+ * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+ * to activation at input gate.
+ * * 24:The forget layer normalization weights.
+ * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+ * to activation at forget gate.
+ * * 25:The cell layer normalization weights.
+ * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+ * to activation at cell gate.
+ * * 26:The output layer normalization weights.
+ * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+ * to activation at output gate.
+ *
+ * Outputs:
+ * * 0: The scratch buffer.
+ * A 2-D tensor of shape [batch_size, num_units * 3] with CIFG, or
+ * [batch_size, num_units * 4] without CIFG.
+ * * 1: The output state (out) (\f$h_t\f$).
+ * A 2-D tensor of shape [batch_size, output_size].
+ * * 2: The cell state (out) (\f$C_t\f$).
+ * A 2-D tensor of shape [batch_size, num_units].
+ * * 3: The output (\f$o_t\f$).
+ * A 2-D tensor of shape [batch_size, output_size]. This is effectively
+ * the same as the current “output state (out)” value.
+ *
+ * Available since API level 27.
+ */
+ LSTM = @1.1::OperationType:LSTM,
+
+ /**
+ * Performs an 2-D max pooling operation.
+ *
+ * The output dimensions are functions of the filter dimensions, stride, and
+ * padding.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[b, i, j, channel] =
+ * max_{di, dj} (
+ * input[b, strides[1] * i + di, strides[2] * j + dj, channel]
+ * )
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ *
+ * Both explicit padding and implicit padding are supported.
+ *
+ * Inputs (explicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * the input.
+ * * 1: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the left, in the ‘width’ dimension.
+ * * 2: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the right, in the ‘width’ dimension.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the top, in the ‘height’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the bottom, in the ‘height’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the filter
+ * width.
+ * * 8: An {@link OperandType::INT32} scalar, specifying the filter
+ * height.
+ * * 9: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 10: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * Available since API level 29.
+ *
+ * Inputs (implicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * the input.
+ * * 1: An {@link OperandType::INT32} scalar, specifying the implicit
+ * padding scheme, has to be one of the
+ * following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+ * * 2: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the filter
+ * width.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the filter
+ * height.
+ * * 6: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 7: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * Available since API level 29.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape
+ * [batches, out_height, out_width, depth].
+ *
+ * Available since API level 27.
+ */
+ MAX_POOL_2D = @1.1::OperationType:MAX_POOL_2D,
+
+ /**
+ * Multiplies two tensors, element-wise.
+ *
+ * Takes two input tensors of identical {@link OperandType} and compatible
+ * dimensions. The output is the product of both input tensors, optionally
+ * modified by an activation function.
+ *
+ * Two dimensions are compatible when:
+ * 1. they are equal, or
+ * 2. one of them is 1
+ *
+ * The size of the resulting output is the maximum size along each dimension
+ * of the input operands. It starts with the trailing dimensions, and works
+ * its way forward.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
+ * as input0.
+ * * 2: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ *
+ * Outputs:
+ * * 0: The product, a tensor of the same {@link OperandType} as input0.
+ * For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the following condition must be satisfied:
+ * output_scale > input1_scale * input2_scale.
+ *
+ * Available since API level 27.
+ */
+ MUL = @1.1::OperationType:MUL,
+
+ /**
+ * Computes rectified linear activation on the input tensor element-wise.
+ *
+ * The output is calculated using this formula:
+ *
+ * output = max(0, input)
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ *
+ * Available since API level 27.
+ */
+ RELU = @1.1::OperationType:RELU,
+
+ /**
+ * Computes rectified linear 1 activation on the input tensor element-wise.
+ *
+ * The output is calculated using this formula:
+ *
+ * output = min(1.f, max(-1.f, input))
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ *
+ * Available since API level 27.
+ */
+ RELU1 = @1.1::OperationType:RELU1,
+
+ /**
+ * Computes rectified linear 6 activation on the input tensor element-wise.
+ *
+ * The output is calculated using this formula:
+ *
+ * output = min(6, max(0, input))
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ *
+ * Available since API level 27.
+ */
+ RELU6 = @1.1::OperationType:RELU6,
+
+ /**
+ * Reshapes a tensor.
+ *
+ * Given tensor, this operation returns a tensor that has the same values as
+ * tensor, but with a newly specified shape.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the tensor to be reshaped.
+ * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}, defining the
+ * shape of the output tensor. The number of elements implied by shape
+ * must be the same as the number of elements in the input tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor, of shape specified by the input shape.
+ *
+ * Available since API level 27.
+ */
+ RESHAPE = @1.1::OperationType:RESHAPE,
+
+ /**
+ * Resizes images to given size using the bilinear interpretation.
+ *
+ * Resized images must be distorted if their output aspect ratio is not the
+ * same as input aspect ratio. The corner pixels of output may not be the
+ * same as corner pixels of input.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ *
+ * Inputs:
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * the input.
+ * * 1: An {@link OperandType::INT32} scalar, specifying the output
+ * height of the output tensor.
+ * * 2: An {@link OperandType::INT32} scalar, specifying the output
+ * width of the output tensor.
+ * * 3: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * Available since API level 29.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape
+ * [batches, new_height, new_width, depth].
+ *
+ * Available since API level 27.
+ */
+ RESIZE_BILINEAR = @1.1::OperationType:RESIZE_BILINEAR,
+
+ /**
+ * A basic recurrent neural network layer.
+ *
+ * This layer implements the operation:
+ * outputs = state = activation(inputs * input_weights +
+ * state * recurrent_weights + bias)
+ *
+ * Where:
+ * * “input_weights” is a weight matrix that multiplies the inputs;
+ * * “recurrent_weights” is a weight matrix that multiplies the current
+ * “state” which itself is the output from the previous time step
+ * computation;
+ * * “bias” is a bias vector (added to each output vector in the batch);
+ * * “activation” is the function passed as the “fused_activation_function”
+ * argument (if not “NONE”).
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * The input tensors must all be the same type.
+ *
+ * Inputs:
+ * * 0: input.
+ * A 2-D tensor of shape [batch_size, input_size], where “batch_size”
+ * corresponds to the batching dimension, and “input_size” is the size
+ * of the input.
+ * * 1: weights.
+ * A 2-D tensor of shape [num_units, input_size], where “num_units”
+ * corresponds to the number of units.
+ * * 2: recurrent_weights.
+ * A 2-D tensor of shape [num_units, num_units], with columns
+ * corresponding to the weights from each unit.
+ * * 3: bias.
+ * A 1-D tensor of shape [num_units].
+ * * 4: hidden state (in).
+ * A 2-D tensor of shape [batch_size, num_units].
+ * * 5: fused_activation_function.
+ * An optional {@link FusedActivationFunc} value indicating the
+ * activation function. If “NONE” is specified then it results in a
+ * linear activation.
+ *
+ * Outputs:
+ * * 0: hidden state (out).
+ * A 2-D tensor of shape [batch_size, num_units].
+ *
+ * * 1: output.
+ * A 2-D tensor of shape [batch_size, num_units]. This is effectively
+ * the same as the current state value.
+ *
+ * Available since API level 27.
+ */
+ RNN = @1.1::OperationType:RNN,
+
+ /**
+ * Computes the softmax activation on the input tensor element-wise, per
+ * batch, by normalizing the input vector so the maximum coefficient is
+ * zero.
+ *
+ * The output is calculated using this formula:
+ *
+ * output[batch, i] =
+ * exp((input[batch, i] - max(input[batch, :])) * beta) /
+ * sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)}
+ *
+ * For input tensor with rank other than 2, the activation will be applied
+ * independently on each 1-D slice along specified dimension.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4.
+ * Tensors with rank other than 2 or 4 are only supported since API level 29.
+ *
+ * Inputs:
+ * * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
+ * * 1: An {@link OperandType::FLOAT32} scalar, specifying the positive
+ * scaling factor for the exponent, beta.
+ * * 2: An optional {@link OperandType::INT32} scalar, default to -1,
+ * specifying the dimension the activation would be performed on.
+ * Negative index is used to specify axis from the end (e.g. -1 for
+ * the last axis). Must be in the range [-n, n).
+ * Available since API level 29.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ * For {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the scale must be 1.f / 256 and the zeroPoint must be 0.
+ *
+ * Available since API level 27.
+ */
+ SOFTMAX = @1.1::OperationType:SOFTMAX,
+
+ /**
+ * Rearranges blocks of spatial data, into depth.
+ *
+ * More specifically, this op outputs a copy of the input tensor where
+ * values from the height and width dimensions are moved to the depth
+ * dimension. The value block_size indicates the input block size and how
+ * the data is moved.
+ *
+ * Chunks of data of size block_size * block_size from depth are rearranged
+ * into non-overlapping blocks of size block_size x block_size.
+ *
+ * The depth of the output tensor is input_depth * block_size * block_size.
+ * The input tensor's height and width must be divisible by block_size.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ *
+ * Inputs:
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+ * specifying the input.
+ * * 1: An {@link OperandType::INT32} scalar, specifying the block_size.
+ * block_size must be >=1 and block_size must be a divisor of both the
+ * input height and width.
+ * * 2: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * Available since API level 29.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape [batches, height/block_size,
+ * width/block_size, depth_in*block_size*block_size].
+ *
+ * Available since API level 27.
+ */
+ SPACE_TO_DEPTH = @1.1::OperationType:SPACE_TO_DEPTH,
+
+ /**
+ * SVDF op is a kind of stateful layer derived from the notion that a
+ * densely connected layer that's processing a sequence of input frames can
+ * be approximated by using a singular value decomposition of each of its
+ * nodes. The implementation is based on:
+ *
+ * https://research.google.com/pubs/archive/43813.pdf
+ *
+ * P. Nakkiran, R. Alvarez, R. Prabhavalkar, C. Parada.
+ * “Compressing Deep Neural Networks using a Rank-Constrained Topology”.
+ * INTERSPEECH, 2015.
+ *
+ * It processes the incoming input using a 2-stage filtering mechanism:
+ * * stage 1 performs filtering on the "features" dimension, whose outputs
+ * get pushed into a memory of fixed-size memory_size.
+ * * stage 2 performs filtering on the "time" dimension of the memory_size
+ * memoized outputs of stage 1.
+ *
+ * Specifically, for rank 1, this layer implements the operation:
+ *
+ * memory = push(conv1d(inputs, weights_feature, feature_dim,
+ * "PADDING_VALID"));
+ * outputs = activation(memory * weights_time + bias);
+ *
+ * Where:
+ * * “weights_feature” is a weights matrix that processes the inputs (by
+ * convolving the input with every “feature filter”), and whose outputs
+ * get pushed, stacked in order, into the fixed-size “memory” (the oldest
+ * entry gets dropped);
+ * * “weights_time” is a weights matrix that processes the “memory” (by a
+ * batched matrix multiplication on the num_units);
+ * * “bias” is an optional bias vector (added to each output vector in the
+ * batch); and
+ * * “activation” is the function passed as the “fused_activation_function”
+ * argument (if not “NONE”).
+ *
+ * Each rank adds a dimension to the weights matrices by means of stacking
+ * the filters.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * All input tensors must be the same type.
+ *
+ * Inputs:
+ * * 0: input.
+ * A 2-D tensor of shape [batch_size, input_size], where “batch_size”
+ * corresponds to the batching dimension, and “input_size” is the size
+ * of the input.
+ * * 1: weights_feature.
+ * A 2-D tensor of shape [num_units, input_size], where “num_units”
+ * corresponds to the number of units.
+ * * 2: weights_time.
+ * A 2-D tensor of shape [num_units, memory_size], where “memory_size”
+ * corresponds to the fixed-size of the memory.
+ * * 3: bias.
+ * An optional 1-D tensor of shape [num_units].
+ * * 4: state (in).
+ * A 2-D tensor of shape [batch_size, (memory_size - 1) * num_units * rank].
+ * * 5: rank.
+ * The rank of the SVD approximation.
+ * * 6: fused_activation_function.
+ * An optional {@link FusedActivationFunc} value indicating the
+ * activation function. If “NONE” is specified then it results in a
+ * linear activation.
+ *
+ * Outputs:
+ * * 0: state (out).
+ * A 2-D tensor of the same {@link OperandType} as the inputs, with shape
+ * [batch_size, (memory_size - 1) * num_units * rank].
+ * * 1: output.
+ * A 2-D tensor of the same {@link OperandType} as the inputs, with shape
+ * [batch_size, num_units].
+ *
+ * Available since API level 27.
+ */
+ SVDF = @1.1::OperationType:SVDF,
+
+ /**
+ * Computes hyperbolic tangent of input tensor element-wise.
+ *
+ * The output is calculated using this formula:
+ *
+ * output = tanh(input)
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since API level 29)
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ * For {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the scale must be 1.f / 128 and the zeroPoint must be 128.
+ *
+ * Available since API level 27.
+ */
+ TANH = @1.1::OperationType:TANH,
+
+ /**
+ * BatchToSpace for N-dimensional tensors.
+ *
+ * This operation reshapes the batch dimension (dimension 0) into M + 1
+ * dimensions of shape block_shape + [batch], interleaves these blocks back
+ * into the grid defined by the spatial dimensions [1, ..., M], to obtain a
+ * result with the same rank as the input.
+ *
+ * This is the reverse of SpaceToBatch.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ *
+ * Inputs:
+ * * 0: An n-D tensor, specifying the tensor to be reshaped
+ * * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}, the block
+ * sizes for each spatial dimension of the input tensor. All values
+ * must be >= 1.
+ * * 2: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * Available since API level 29.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ *
+ * Available since API level 28.
+ */
+ BATCH_TO_SPACE_ND = @1.1::OperationType:BATCH_TO_SPACE_ND,
+
+ /**
+ * Element-wise division of two tensors.
+ *
+ * Takes two input tensors of identical {@link OperandType} and compatible
+ * dimensions. The output is the result of dividing the first input tensor
+ * by the second, optionally modified by an activation function.
+ *
+ * Two dimensions are compatible when:
+ * 1. they are equal, or
+ * 2. one of them is 1
+ *
+ * The size of the output is the maximum size along each dimension of the
+ * input operands. It starts with the trailing dimensions, and works its way
+ * forward.
+ *
+ * Example:
+ * input1.dimension = {4, 1, 2}
+ * input2.dimension = {5, 4, 3, 1}
+ * output.dimension = {5, 4, 3, 2}
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor, specifying the first input.
+ * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
+ * as input0.
+ * * 2: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ *
+ * Available since API level 28.
+ */
+ DIV = @1.1::OperationType:DIV,
+
+ /**
+ * Computes the mean of elements across dimensions of a tensor.
+ *
+ * Reduces the input tensor along the given dimensions to reduce. Unless
+ * keep_dims is true, the rank of the tensor is reduced by 1 for each entry
+ * in axis. If keep_dims is true, the reduced dimensions are retained with
+ * length 1.
+ *
+ * If dimensions to reduce have no entries, all dimensions are reduced, and
+ * a tensor with a single element is returned.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input.
+ * * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}. The dimensions
+ * to reduce. If None (the default), reduces all dimensions. Must be in
+ * the range [-rank(input_tensor), rank(input_tensor)).
+ * * 2: An {@link OperandType::INT32} scalar, keep_dims. If positive,
+ * retains reduced dimensions with length 1.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ *
+ * Available since API level 28.
+ */
+ MEAN = @1.1::OperationType:MEAN,
+
+ /**
+ * Pads a tensor with zeros.
+ *
+ * This operation pads a tensor according to the specified paddings.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor, specifying the tensor to be padded.
+ * * 1: A 2-D Tensor of {@link OperandType::TENSOR_INT32}, the paddings
+ * for each spatial dimension of the input tensor. The shape of the
+ * tensor must be {rank(input0), 2}.
+ * padding[i, 0] specifies the number of elements to be padded in the
+ * front of dimension i.
+ * padding[i, 1] specifies the number of elements to be padded after the
+ * end of dimension i.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0. The
+ * output tensor has the same rank as input0, and each
+ * dimension of the output tensor has the same size as the
+ * corresponding dimension of the input tensor plus the size
+ * of the padding:
+ * output0.dimension[i] =
+ * padding[i, 0] + input0.dimension[i] + padding[i, 1]
+ *
+ * Available since API level 28.
+ */
+ PAD = @1.1::OperationType:PAD,
+
+ /**
+ * SpaceToBatch for N-Dimensional tensors.
+ *
+ * This operation divides "spatial" dimensions [1, ..., M] of the input into
+ * a grid of blocks of shape block_shape, and interleaves these blocks with
+ * the "batch" dimension (0) such that in the output, the spatial dimensions
+ * [1, ..., M] correspond to the position within the grid, and the batch
+ * dimension combines both the position within a spatial block and the
+ * original batch position. Prior to division into blocks, the spatial
+ * dimensions of the input are optionally zero padded according to paddings.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ *
+ * Inputs:
+ * * 0: An n-D tensor, specifying the input.
+ * * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}, the block
+ * sizes for each spatial dimension of the input tensor. All values
+ * must be >= 1.
+ * * 2: A 2-D Tensor of {@link OperandType::TENSOR_INT32}, the paddings
+ * for each spatial dimension of the input tensor. All values must be
+ * >= 0. The shape of the tensor must be {rank(input0), 2}.
+ * padding[i, 0] specifies the number of element to be padded in the
+ * front of dimension i.
+ * padding[i, 1] specifies the number of element to be padded after the
+ * end of dimension i.
+ * * 3: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * Available since API level 29.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ *
+ * Available since API level 28.
+ */
+ SPACE_TO_BATCH_ND = @1.1::OperationType:SPACE_TO_BATCH_ND,
+
+ /**
+ * Removes dimensions of size 1 from the shape of a tensor.
+ *
+ * Given a tensor input, this operation returns a tensor of the same
+ * {@link OperandType} with all dimensions of size 1 removed. If you don't
+ * want to remove all size 1 dimensions, you can remove specific size 1
+ * dimensions by specifying the axes (input1).
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor, the tensor to be squeezed.
+ * * 1: An optional 1-D tensor of {@link OperandType::TENSOR_INT32}. The
+ * dimensions to squeeze. If specified only squeezes the dimensions
+ * listed. Otherwise, squeezes all dimensions. The dimension index
+ * starts at 0. An error must be reported if squeezing a dimension that
+ * is not 1.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0. Contains the
+ * same data as input, but has one or more dimensions of size 1
+ * removed.
+ *
+ * Available since API level 28.
+ */
+ SQUEEZE = @1.1::OperationType:SQUEEZE,
+
+ /**
+ * Extracts a strided slice of a tensor.
+ *
+ * Roughly speaking, this op extracts a slice of size (end - begin) / stride
+ * from the given input tensor. Starting at the location specified by begin
+ * the slice continues by adding stride to the index until all dimensions
+ * are not less than end. Note that a stride can be negative, which causes a
+ * reverse slice.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor, specifying the tensor to be sliced.
+ * * 1: begin, a 1-D tensor of {@link OperandType::TENSOR_INT32}. The
+ * starts of the dimensions of the input tensor to be sliced. The
+ * length must be of rank(input0).
+ * * 2: end, a 1-D tensor of {@link OperandType::TENSOR_INT32}. The
+ * ends of the dimensions of the input tensor to be sliced. The length
+ * must be of rank(input0).
+ * * 3: strides, a 1-D tensor of {@link OperandType::TENSOR_INT32}. The
+ * strides of the dimensions of the input tensor to be sliced. The
+ * length must be of rank(input0). The entries must be non-zero.
+ * * 4: begin_mask, an {@link OperandType::INT32} scalar. If the ith bit
+ * of begin_mask is set, begin[i] is ignored and the fullest possible
+ * range in that dimension is used instead.
+ * * 5: end_mask, an {@link OperandType::INT32} scalar. If the ith bit of
+ * end_mask is set, end[i] is ignored and the fullest possible range in
+ * that dimension is used instead.
+ * * 6: shrink_axis_mask, an {@link OperandType::INT32} scalar. If the
+ * ith bit of shrink_axis_mask is set, the ith dimension specification
+ * shrinks the dimensionality by 1, taking on the value at index
+ * begin[i]. In this case, the ith specification must define a
+ * slice of size 1, e.g. begin[i] = x, end[i] = x + 1.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0 and rank (n - k),
+ * where k is the number of bits set in shrink_axis_mask.
+ *
+ * Available since API level 28.
+ */
+ STRIDED_SLICE = @1.1::OperationType:STRIDED_SLICE,
+
+ /**
+ * Element-wise subtraction of two tensors.
+ *
+ * Takes two input tensors of identical {@link OperandType} and compatible
+ * dimensions. The output is the result of subtracting the second input
+ * tensor from the first one, optionally modified by an activation function.
+ *
+ * Two dimensions are compatible when:
+ * 1. they are equal, or
+ * 2. one of them is 1
+ *
+ * The size of the output is the maximum size along each dimension of the
+ * input operands. It starts with the trailing dimensions, and works its way
+ * forward.
+ *
+ * Example:
+ * input1.dimension = {4, 1, 2}
+ * input2.dimension = {5, 4, 3, 1}
+ * output.dimension = {5, 4, 3, 2}
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since API level 29)
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor, specifying the first input.
+ * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
+ * as input0.
+ * * 2: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ *
+ * Available since API level 28.
+ */
+ SUB = @1.1::OperationType:SUB,
+
+ /**
+ * Transposes the input tensor, permuting the dimensions according to the
+ * perm tensor.
+ *
+ * The returned tensor's dimension i corresponds to the input dimension
+ * perm[i]. If perm is not given, it is set to (n-1...0), where n is the
+ * rank of the input tensor. Hence by default, this operation performs a
+ * regular matrix transpose on 2-D input Tensors.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor, specifying the tensor to be transposed.
+ * * 1: An optional 1-D Tensor of {@link OperandType::TENSOR_INT32},
+ * the permutation of the dimensions of the input tensor.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ *
+ * Available since API level 28.
+ */
+ TRANSPOSE = @1.1::OperationType:TRANSPOSE,
+
+ /**
+ * Computes the absolute value of a tensor, element-wise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ *
+ * Available since API level 29.
+ */
ABS = 38,
+
+ /**
+ * Returns the index of the largest element along an axis.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: An n-D tensor specifying the input. Must be non-empty.
+ * * 1: An {@link OperandType::INT32} scalar specifying the axis to
+ * reduce across. Negative index is used to specify axis from the
+ * end (e.g. -1 for the last axis). Must be in the range [-n, n).
+ *
+ * Outputs:
+ * * 0: An (n - 1)-D {@link OperandType::TENSOR_INT32} tensor.
+ *
+ * Available since API level 29.
+ */
+ // There is no underscore in ARG_MAX to avoid name conflict with
+ // the macro defined in libc/kernel/uapi/linux/limits.h.
ARGMAX = 39,
- ARGMIN = 40,
+
+ /**
+ * Returns the index of the smallest element along an axis.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: An n-D tensor specifying the input. Must be non-empty.
+ * * 1: An {@link OperandType::INT32} scalar specifying the axis to
+ * reduce across. Negative index is used to specify axis from the
+ * end (e.g. -1 for the last axis). Must be in the range [-n, n).
+ *
+ * Outputs:
+ * * 0: An (n - 1)-D {@link OperandType::TENSOR_INT32} tensor.
+ *
+ * Available since API level 29.
+ */
+ ARGMIN = 40, // See ARGMAX for naming discussion.
+
+ /**
+ * Transform axis-aligned bounding box proposals using bounding box deltas.
+ *
+ * Given the positions of bounding box proposals and the corresponding
+ * bounding box deltas for each class, return the refined bounding box
+ * regions. The resulting bounding boxes are cliped against the edges of
+ * the image.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Inputs:
+ * * 0: A 2-D Tensor of shape [num_rois, 4], specifying the locations of the
+ * bounding box proposals, each line with format [x1, y1, x2, y2].
+ * For tensor of type {@link OperandType::TENSOR_QUANT16_ASYMM},
+ * the zeroPoint must be 0 and the scale must be 0.125.
+ * * 1: A 2-D Tensor of shape [num_rois, num_classes * 4], specifying the
+ * bounding box delta for each region of interest and each class. The
+ * bounding box deltas are organized in the following order
+ * [dx, dy, dw, dh], where dx and dy is the relative correction factor
+ * for the center position of the bounding box with respect to the width
+ * and height, dw and dh is the log-scale relative correction factor
+ * for the width and height. For input0 of type
+ * {@link OperandType::TENSOR_QUANT16_ASYMM}, this tensor should be
+ * of {@link OperandType::TENSOR_QUANT8_ASYMM}.
+ * * 2: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
+ * [batches], specifying the number of output boxes for each batch.
+ * * 3: A 2-D Tensor of shape [batches, 2], specifying the information of
+ * each image in the batch, each line with format
+ * [image_height, image_width].
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0, with shape
+ * [num_rois, num_classes * 4], specifying the coordinates of each
+ * output bounding box for each class, with format [x1, y1, x2, y2].
+ *
+ * Available since API level 29.
+ */
AXIS_ALIGNED_BBOX_TRANSFORM = 41,
+
+ /**
+ * Performs a forward LSTM on the input followed by a backward LSTM.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: 3, either time-major or batch-major.
+ *
+ * All input and output tensors must be of the same type.
+ *
+ *
+ * Inputs:
+ * * 0: The input.
+ * A 3-D tensor of shape:
+ * If time-major: [max_time, batch_size, output_size]
+ * If batch-major: [batch_size, max_time, output_size]
+ * where "max_time" is the number of timesteps (sequence length),
+ * "batch_size" corresponds to the batching dimension, and
+ * "input_size" is the size of the input.
+ * * 1: The forward input-to-input weights. Optional.
+ * A 2-D tensor of shape [num_units, input_size], where “num_units”
+ * corresponds to the number of cell units.
+ * * 2: The forward input-to-forget weights.
+ * A 2-D tensor of shape [num_units, input_size].
+ * * 3: The forward input-to-cell weights.
+ * A 2-D tensor of shape [num_units, input_size].
+ * * 4: The forward input-to-output weights.
+ * A 2-D tensor of shape [num_units, input_size].
+ * * 5: The forward recurrent-to-input weights. Optional.
+ * A 2-D tensor of shape [num_units, output_size], where “output_size”
+ * corresponds to either the number of cell units (i.e., “num_units”),
+ * or the second dimension of the “projection_weights”, if defined.
+ * * 6: The forward recurrent-to-forget weights.
+ * A 2-D tensor of shape [num_units, output_size].
+ * * 7: The forward recurrent-to-cell weights.
+ * A 2-D tensor of shape [num_units, output_size].
+ * * 8: The forward recurrent-to-output weights.
+ * A 2-D tensor of shape [num_units, output_size].
+ * * 9: The forward cell-to-input weights. Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 10: The forward cell-to-forget weights. Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 11: The forward cell-to-output weights. Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 12: The forward input gate bias. Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 13: The forward forget gate bias.
+ * A 1-D tensor of shape [num_units].
+ * * 14: The forward cell gate bias.
+ * A 1-D tensor of shape [num_units].
+ * * 15: The forward output gate bias.
+ * A 1-D tensor of shape [num_units].
+ * * 16: The forward projection weights. Optional.
+ * A 2-D tensor of shape [output_size, num_units].
+ * * 17: The forward projection bias. Optional.
+ * A 1-D tensor of shape [output_size].
+ * * 18: The backward input-to-input weights. Optional.
+ * A 2-D tensor of shape [num_units, input_size], where “num_units”
+ * corresponds to the number of cell units.
+ * * 19: The backward input-to-forget weights.
+ * A 2-D tensor of shape [num_units, input_size].
+ * * 20: The backward input-to-cell weights.
+ * A 2-D tensor of shape [num_units, input_size].
+ * * 21: The backward input-to-output weights.
+ * A 2-D tensor of shape [num_units, input_size].
+ * * 22: The backward recurrent-to-input weights. Optional.
+ * A 2-D tensor of shape [num_units, output_size], where “output_size”
+ * corresponds to either the number of cell units (i.e., “num_units”),
+ * or the second dimension of the “projection_weights”, if defined.
+ * * 23: The backward recurrent-to-forget weights.
+ * A 2-D tensor of shape [num_units, output_size].
+ * * 24: The backward recurrent-to-cell weights.
+ * A 2-D tensor of shape [num_units, output_size].
+ * * 25: The backward recurrent-to-output weights.
+ * A 2-D tensor of shape [num_units, output_size].
+ * * 26: The backward cell-to-input weights. Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 27: The backward cell-to-forget weights. Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 28: The backward cell-to-output weights. Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 29: The backward input gate bias. Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 30: The backward forget gate bias.
+ * A 1-D tensor of shape [num_units].
+ * * 31: The backward cell gate bias.
+ * A 1-D tensor of shape [num_units].
+ * * 32: The backward output gate bias.
+ * A 1-D tensor of shape [num_units].
+ * * 33: The backward projection weights. Optional.
+ * A 2-D tensor of shape [output_size, num_units].
+ * * 34: The backward projection bias. Optional.
+ * A 1-D tensor of shape [output_size].
+ * * 35: The forward input activation state.
+ * A 2-D tensor of shape [batch_size, output_size].
+ * * 36: The forward input cell state.
+ * A 2-D tensor of shape [batch_size, num_units].
+ * * 37: The backward input activation state.
+ * A 2-D tensor of shape [batch_size, output_size].
+ * * 38: The backward input cell state.
+ * A 2-D tensor of shape [batch_size, num_units].
+ * * 39: The auxiliary input. Optional.
+ * A 3-D tensor of shape [max_time, batch_size, input_size], where “batch_size”
+ * corresponds to the batching dimension, and “input_size” is the size
+ * of the input.
+ * * 40: The forward auxiliary input-to-input weights. Optional.
+ * A 2-D tensor of shape [num_units, input_size].
+ * * 41: The forward auxiliary input-to-forget weights. Optional.
+ * A 2-D tensor of shape [num_units, input_size].
+ * * 42: The forward auxiliary input-to-cell weights. Optional.
+ * A 2-D tensor of shape [num_units, input_size].
+ * * 43: The forward auxiliary input-to-output weights. Optional.
+ * A 2-D tensor of shape [num_units, input_size].
+ * * 44: The backward auxiliary input-to-input weights. Optional.
+ * A 2-D tensor of shape [num_units, input_size].
+ * * 45: The backward auxiliary input-to-forget weights. Optional.
+ * A 2-D tensor of shape [num_units, input_size].
+ * * 46: The backward auxiliary input-to-cell weights. Optional.
+ * A 2-D tensor of shape [num_units, input_size].
+ * * 47: The backward auxiliary input-to-output weights. Optional.
+ * A 2-D tensor of shape [num_units, input_size].
+ * * 48: The activation function.
+ * A value indicating the activation function:
+ * <ul>
+ * <li>0: None;
+ * <li>1: Relu;
+ * <li>3: Relu6;
+ * <li>4: Tanh;
+ * <li>6: Sigmoid.
+ * </ul>
+ * * 49: The clipping threshold for the cell state, such
+ * that values are bound within [-cell_clip, cell_clip]. If set to 0.0
+ * then clipping is disabled.
+ * If all the input tensors have type {@link OperandType::TENSOR_FLOAT32},
+ * this scalar must be of the type {@link OperandType::FLOAT32},
+ * otherwise if all the input tensors have the type {@link
+ * TENSOR_FLOAT16}, this scalar must be of type {@link
+ * FLOAT16}.
+ * * 50: The clipping threshold for the output from the
+ * projection layer, such that values are bound within
+ * [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
+ * If all the input tensors have type {@link OperandType::TENSOR_FLOAT32},
+ * this scalar must be of the type {@link OperandType::FLOAT32},
+ * otherwise if all the input tensors have the type {@link
+ * TENSOR_FLOAT16}, this scalar must be of type {@link
+ * FLOAT16}.
+ * * 51: merge_outputs
+ * An {@link OperandType::BOOL} scalar specifying if the outputs
+ * from forward and backward cells should be merged.
+ * * 52: time_major
+ * An {@link OperandType::BOOL} scalar specifying the shape format
+ * of input and output tensors.
+ *
+ * Outputs:
+ * * 0: The forward output.
+ * A 3-D tensor of shape:
+ * If time-major: [max_time, batch_size, output_size]
+ * If batch-major: [batch_size, max_time, output_size]
+ * * 1: The backward output. Unused if merge_outputs is true.
+ * A 3-D tensor of shape:
+ * If time-major: [max_time, batch_size, output_size]
+ * If batch-major: [batch_size, max_time, output_size]
+ *
+ * Available since API level 29.
+ */
BIDIRECTIONAL_SEQUENCE_LSTM = 42,
+
+ /**
+ * A recurrent neural network layer that applies a basic RNN cell to a
+ * sequence of inputs in forward and backward directions.
+ *
+ * This Op unrolls the input along the sequence dimension, and implements
+ * the following operation for each element in the sequence s =
+ * 1...sequence_length:
+ * fw_outputs[s] = fw_state = activation(inputs[s] * fw_input_weights’ +
+ * fw_state * fw_recurrent_weights’ + fw_bias)
+ *
+ * And for each element in sequence t = sequence_length : 1
+ * bw_outputs[t] = bw_state = activation(inputs[t] * bw_input_weights’ +
+ * bw_state * bw_recurrent_weights’ + bw_bias)
+ *
+ * Where:
+ * * “{fw,bw}_input_weights” is a weight matrix that multiplies the inputs;
+ * * “{fw,bw}_recurrent_weights” is a weight matrix that multiplies the
+ * current “state” which itself is the output from the previous time step
+ * computation;
+ * * “{fw,bw}_bias” is a bias vector (added to each output vector in the
+ * batch);
+ * * “activation” is the function passed as the “fused_activation_function”
+ * argument (if not “NONE”).
+ *
+ * The op also supports an auxiliary input. Regular cell feeds one input
+ * into the two RNN cells in the following way:
+ *
+ * INPUT (INPUT_REVERSED)
+ * | |
+ * ---------------------
+ * | FW_RNN BW_RNN |
+ * ---------------------
+ * | |
+ * FW_OUT BW_OUT
+ *
+ * An op with an auxiliary input takes two inputs and feeds them into the
+ * RNN cells in the following way:
+ *
+ * AUX_INPUT (AUX_INPUT_REVERSED)
+ * | |
+ * INPUT | (INPUT_R'D.)|
+ * | | | |
+ * -----------------------
+ * | \ / \ / |
+ * | FW_RNN BW_RNN |
+ * -----------------------
+ * | |
+ * FW_OUT BW_OUT
+ *
+ * While stacking this op on top of itself, this allows to connect both
+ * forward and backward outputs from previous cell to the next cell's
+ * inputs.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * The input tensors must all be the same type.
+ *
+ * Inputs:
+ * * 0: input.
+ * A 3-D tensor. The shape is defined by the input 6 (timeMajor). If
+ * it is set to true, then the input has a shape [maxTime, batchSize,
+ * inputSize], otherwise the input has a shape [batchSize, maxTime,
+ * inputSize].
+ * * 1: fwWeights.
+ * A 2-D tensor of shape [fwNumUnits, inputSize].
+ * * 2: fwRecurrentWeights.
+ * A 2-D tensor of shape [fwNumUnits, fwNumUnits].
+ * * 3: fwBias.
+ * A 1-D tensor of shape [fwNumUnits].
+ * * 4: fwHiddenState.
+ * A 2-D tensor of shape [batchSize, fwNumUnits]. Specifies a hidden
+ * state input for the first time step of the computation.
+ * * 5: bwWeights.
+ * A 2-D tensor of shape [bwNumUnits, inputSize].
+ * * 6: bwRecurrentWeights.
+ * A 2-D tensor of shape [bwNumUnits, bwNumUnits].
+ * * 7: bwBias.
+ * A 1-D tensor of shape [bwNumUnits].
+ * * 8: bwHiddenState
+ * A 2-D tensor of shape [batchSize, bwNumUnits]. Specifies a hidden
+ * state input for the first time step of the computation.
+ * * 9: auxInput.
+ * A 3-D tensor. The shape is the same as of the input 0.
+ * * 10:fwAuxWeights.
+ * A 2-D tensor of shape [fwNumUnits, inputSize].
+ * * 11:bwAuxWeights.
+ * A 2-D tensor of shape [bwNumUnits, inputSize].
+ * * 12:fusedActivationFunction.
+ * A {@link FusedActivationFunc} value indicating the activation function. If
+ * “NONE” is specified then it results in a linear activation.
+ * * 13:timeMajor
+ * An {@link OperandType::BOOL} scalar specifying the shape format
+ * of input and output tensors.
+ * * 14:mergeOutputs
+ * An {@link OperandType::BOOL} scalar specifying if the outputs
+ * from forward and backward cells are separate (if set to false) or
+ * concatenated (if set to true).
+ * Outputs:
+ * * 0: fwOutput.
+ * A 3-D tensor. The first two dimensions of the shape are defined by
+ * the input 6 (timeMajor) and the third dimension is defined by the
+ * input 14 (mergeOutputs). If timeMajor is set to true, then the first
+ * two dimensions are [maxTime, batchSize], otherwise they are set to
+ * [batchSize, maxTime]. If mergeOutputs is set to true, then the third
+ * dimension is equal to (fwNumUnits + bwNumUnits), otherwise it is set
+ * to fwNumUnits.
+ * * 1: bwOutput.
+ * A 3-D tensor. If the input 14 (mergeOutputs) is set to true, then
+ * this tensor is not produced. The shape is defined by the input 6
+ * (timeMajor). If it is set to true, then the shape is set to
+ * [maxTime, batchSize, bwNumUnits], otherwise the shape is set to
+ * [batchSize, maxTime, bwNumUnits].
+ *
+ * Available since API level 29.
+ */
BIDIRECTIONAL_SEQUENCE_RNN = 43,
+
+ /**
+ * Greedily selects a subset of bounding boxes in descending order of score.
+ *
+ * This op applies hard NMS algorithm to each class. In each loop of
+ * execution, the box with maximum score gets selected, and any boxes with
+ * the intersection-over-union (IOU) greater than a threshold are removed
+ * from the pending set.
+ *
+ * Axis-aligned bounding boxes are represented by its upper-left corner
+ * coordinate (x1,y1) and lower-right corner coordinate (x2,y2). A valid
+ * bounding box should satisfy x1 <= x2 and y1 <= y2.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Inputs:
+ * * 0: A 2-D Tensor of shape [num_rois, num_classes], specifying the score
+ * of each bounding box proposal. The boxes are grouped by batches in the
+ * first dimension.
+ * * 1: A 2-D Tensor specifying the bounding boxes of shape
+ * [num_rois, num_classes * 4], organized in the order [x1, y1, x2, y2].
+ * The boxes are grouped by batches in the first dimension. The sequential
+ * order of the boxes corresponds with input0. For input0 of type
+ * {@link OperandType::TENSOR_QUANT8_ASYMM}, this tensor should be of
+ * {@link OperandType::TENSOR_QUANT16_ASYMM}, with zeroPoint of 0 and
+ * scale of 0.125.
+ * * 2: A 1-D Tensor of shape [batches], specifying the number of boxes
+ * for each image in the batch.
+ * * 3: An {@link OperandType::FLOAT32} scalar, score_threshold. Boxes
+ * with scores lower than the threshold are filtered before sending
+ * to the NMS algorithm.
+ * * 4: An {@link OperandType::FLOAT32} scalar, specifying the IoU
+ * threshold.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the maximum
+ * number of selected bounding boxes for each image. Set to a negative
+ * value for unlimited number of output bounding boxes.
+ *
+ * Outputs:
+ * * 0: A 1-D Tensor of the same {@link OperandType} as input0, with shape
+ * [num_output_rois], specifying the score of each output box. The boxes
+ * are grouped by batches, but the sequential order in each batch is not
+ * guaranteed. For type of {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the scale and zero point must be the same as input0.
+ * * 1: A 2-D Tensor of the same {@link OperandType} as input1, with shape
+ * [num_output_rois, 4], specifying the coordinates of each
+ * output bounding box with the same format as input1. The sequential
+ * order of the boxes corresponds with output0. For type of
+ * {@link OperandType::TENSOR_QUANT16_ASYMM}, the scale must be
+ * 0.125 and the zero point must be 0.
+ * * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
+ * [num_output_rois], specifying the class of each output box. The
+ * sequential order of the boxes corresponds with output0.
+ * * 3: A 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
+ * [batches], specifying the number of output boxes for each image.
+ *
+ * Available since API level 29.
+ */
BOX_WITH_NMS_LIMIT = 44,
+
+ /**
+ * Casts a tensor to a new type.
+ *
+ * This operation ignores the scale and zeroPoint of quanized tensors,
+ * e.g. it treats a {@link OperandType::TENSOR_QUANT8_ASYMM} input
+ * as a tensor of uint8 values.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: A tensor with the same shape as input0.
+ *
+ * Available since API level 29.
+ */
CAST = 45,
+
+ /**
+ * Shuffle the channels of the input tensor.
+ *
+ * Given an input tensor and a integer value of num_groups, CHANNEL_SHUFFLE
+ * divide the channel dimension into num_groups groups, and reorganize the
+ * channels by grouping channels with the same index in each group.
+ *
+ * Along the channel dimension, the output is calculated using this formula:
+ *
+ * output_channel[k * num_groups + g] = input_channel[g * group_size + k]
+ *
+ * where group_size = num_channels / num_groups
+ *
+ * The number of channels must be divisible by num_groups.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor, specifying the tensor to be shuffled.
+ * * 1: An {@link OperandType::INT32} scalar, specifying the number of
+ * groups.
+ * * 2: An {@link OperandType::INT32} scalar, specifying the dimension
+ * channel shuffle would be performed on. Negative index is used to
+ * specify axis from the end (e.g. -1 for the last axis). Must be in
+ * the range [-n, n).
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} and same shape as input0.
+ *
+ * Available since API level 29.
+ */
CHANNEL_SHUFFLE = 46,
+
+ /**
+ * Apply postprocessing steps to bounding box detections.
+ *
+ * Bounding box detections are generated by applying transformation on a set
+ * of predefined anchors with the bounding box deltas from bounding box
+ * regression. A final step of hard NMS is applied to limit the number of
+ * returned boxes.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Inputs:
+ * * 0: A 3-D Tensor of shape [batches, num_anchors, num_classes], specifying
+ * the score of each anchor with each class. Class 0 for each
+ * [batches, num_anchors, 0] is background and will be ignored.
+ * * 1: A 3-D Tensor of shape [batches, num_anchors, length_box_encoding], with
+ * the first four values in length_box_encoding specifying the bounding
+ * box deltas. The box deltas are encoded in the order of [dy, dx, dh, dw],
+ * where dy and dx is the linear-scale relative correction factor for the
+ * center position of the bounding box with respect to the width and height,
+ * dh and dw is the log-scale relative correction factor for the width and
+ * height. All the entries in length_box_encoding beyond the first four
+ * values are ignored in this operation.
+ * * 2: A 2-D Tensor of shape [num_anchors, 4], specifying the shape of each
+ * predefined anchor, with format [ctr_y, ctr_x, h, w], where ctr_y and
+ * ctr_x are the center position of the box, and h and w are the height
+ * and the width.
+ * * 3: An {@link OperandType::FLOAT32} scalar, specifying the scaling
+ * factor for dy in bounding box deltas.
+ * * 4: An {@link OperandType::FLOAT32} scalar, specifying the scaling
+ * factor for dx in bounding box deltas.
+ * * 5: An {@link OperandType::FLOAT32} scalar, specifying the scaling
+ * factor for dh in bounding box deltas.
+ * * 6: An {@link OperandType::FLOAT32} scalar, specifying the scaling
+ * factor for dw in bounding box deltas.
+ * * 7: An {@link OperandType::BOOL} scalar, set to true to use regular
+ * multi-class NMS algorithm that do NMS separately for each class,
+ * set to false for a faster algorithm that only do one single NMS
+ * using the highest class score..
+ * * 8: An {@link OperandType::INT32} scalar, max_num_detections, specifying
+ * the maximum number of boxes for the output. Boxes with the lowest
+ * scores are discarded to meet the limit.
+ * * 9: An {@link OperandType::INT32} scalar, only used when input7 is
+ * set to false, specifying the maximum number of classes per detection.
+ * * 10: An {@link OperandType::INT32} scalar, only used when input7 is
+ * set to true, specifying the maximum number of detections when
+ * applying NMS algorithm for each single class.
+ * * 11: An {@link OperandType::FLOAT32} scalar, score_threshold. Boxes
+ * with scores lower than the threshold are filtered before sending
+ * to the NMS algorithm.
+ * * 12: An {@link OperandType::FLOAT32} scalar, specifying the IoU
+ * threshold for hard NMS.
+ * * 13: An {@link OperandType::BOOL} scalar, set to true to include
+ * background class in the list of label map for the output, set
+ * to false to not include the background. When the background
+ * class is included, it has label 0 and the output classes start
+ * at 1 in the label map, otherwise, the output classes start at 0.
+ *
+ * Outputs:
+ * * 0: A 2-D tensor of the same {@link OperandType} as input0, with shape
+ * [batches, max_num_detections], specifying the score of each output
+ * detections.
+ * * 1: A 3-D tensor of shape [batches, max_num_detections, 4], specifying the
+ * coordinates of each output bounding box, with format
+ * [y1, x1, y2, x2].
+ * * 2: A 2-D {@link OperandType::TENSOR_INT32} tensor, of shape
+ * [batches, max_num_detections], specifying the class label for each
+ * output detection.
+ * * 3: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape [batches],
+ * specifying the number of valid output detections for each batch.
+ *
+ * Available since API level 29.
+ */
DETECTION_POSTPROCESSING = 47,
+
+ /**
+ * For input tensors x and y, computes x == y elementwise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: from 1
+ *
+ * This operation supports broadcasting.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same {@link OperandType} and dimensions compatible
+ * with input0.
+ *
+ * Outputs:
+ * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+ *
+ * Available since API level 29.
+ */
EQUAL = 48,
+
+ /**
+ * Computes exponential of x element-wise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ *
+ * Available since API level 29.
+ */
EXP = 49,
+
+ /**
+ * Inserts a dimension of 1 into a tensor's shape.
+ *
+ * Given a tensor input, this operation inserts a dimension of 1 at the
+ * given dimension index of input's shape. The dimension index starts at
+ * zero; if you specify a negative dimension index, it is counted backward
+ * from the end.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: An n-D tensor.
+ * * 1: An {@link OperandType::INT32} scalar specifying the dimension
+ * index to expand. Must be in the range [-(n + 1), (n + 1)).
+ *
+ * Outputs:
+ * * 0: An (n + 1)-D tensor with the same {@link OperandType} and data as
+ * input0.
+ *
+ * Available since API level 29.
+ */
EXPAND_DIMS = 50,
+
+ /**
+ * Gathers values along an axis.
+ *
+ * Produces an output tensor with shape
+ * input0.dimension[:axis] + indices.dimension + input0.dimension[axis + 1:]
+ * where:
+ * # Vector indices (output is rank(input0)).
+ * output[a_0, ..., a_n, i, b_0, ..., b_n] =
+ * input0[a_0, ..., a_n, indices[i], b_0, ..., b_n]
+ *
+ * # Higher rank indices (output is rank(input0) + rank(indices) - 1).
+ * output[a_0, ..., a_n, i, ..., j, b_0, ... b_n] =
+ * input0[a_0, ..., a_n, indices[i, ..., j], b_0, ..., b_n]
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: An n-D tensor from which to gather values.
+ * * 1: An {@link OperandType::INT32} scalar specifying the axis.
+ * Negative index is used to specify axis from the end
+ * (e.g. -1 for the last axis). Must be in the range [-n, n).
+ * * 2: A k-D tensor {@link OperandType::TENSOR_INT32} of indices.
+ * The values must be in the bounds of the corresponding dimensions
+ * of input0.
+ *
+ * Outputs:
+ * * 0: An (n + k - 1)-D tensor with the same {@link OperandType} as input0.
+ *
+ * Available since API level 29.
+ */
GATHER = 51,
+
+ /**
+ * Generate aixs-aligned bounding box proposals.
+ *
+ * Bounding box proposals are generated by applying transformation on a set
+ * of predefined anchors with the bounding box deltas from bounding box
+ * regression. A final step of hard NMS is applied to limit the number of
+ * returned boxes.
+ *
+ * Axis-aligned bounding boxes are represented by its upper-left corner
+ * coordinate (x1,y1) and lower-right corner coordinate (x2,y2). A valid
+ * bounding box should satisfy x1 <= x2 and y1 <= y2.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Inputs:
+ * * 0: A 4-D Tensor specifying the score of each anchor at each
+ * location. With "NHWC" data layout, the tensor shape is
+ * [batches, height, width, num_anchors]. With "NCHW" data layout,
+ * the tensor shape is [batches, num_anchors, height, width].
+ * * 1: A 4-D Tensor specifying the bounding box deltas. With "NHWC" data
+ * layout, the tensor shape is [batches, height, width, num_anchors * 4].
+ * With "NCHW" data layout, the tensor shape is
+ * [batches, num_anchors * 4, height, width]. The box deltas are encoded
+ * in the order of [dx, dy, dw, dh], where dx and dy is the linear-scale
+ * relative correction factor for the center position of the bounding box
+ * with respect to the width and height, dw and dh is the log-scale
+ * relative correction factor for the width and height. The last
+ * dimensions is the channel dimension.
+ * * 2: A 2-D Tensor of shape [num_anchors, 4], specifying the shape of each
+ * predefined anchor, with format [x1, y1, x2, y2]. For input0 of type
+ * {@link OperandType::TENSOR_QUANT8_ASYMM}, this tensor should be of
+ * {@link OperandType::TENSOR_QUANT16_SYMM}, with scale of 0.125.
+ * * 3: A 2-D Tensor of shape [batches, 2], specifying the size of
+ * each image in the batch, with format [image_height, image_width].
+ * * 4: An {@link OperandType::FLOAT32} scalar, specifying the ratio
+ * from the height of original image to the height of feature map.
+ * * 5: An {@link OperandType::FLOAT32} scalar, specifying the ratio
+ * from the width of original image to the width of feature map.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the maximum
+ * number of boxes before going into the hard NMS algorithm. Boxes
+ * with the lowest scores are discarded to meet the limit. Set to
+ * a non-positive value for unlimited number.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the maximum
+ * number of boxes returning from the hard NMS algorithm. Boxes
+ * with the lowest scores are discarded to meet the limit. Set to
+ * a non-positive value for unlimited number.
+ * * 8: An {@link OperandType::FLOAT32} scalar, specifying the IoU
+ * threshold for hard NMS.
+ * * 9: An {@link OperandType::FLOAT32} scalar, min_size. Boxes with
+ * height or width lower than the absolute threshold are filtered out.
+ * * 10: An {@link OperandType::BOOL} scalar, set to true to specify
+ * NCHW data layout for input0 and input1. Set to false for NHWC.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0, of shape
+ * [num_output_rois], specifying the score of each output box.
+ * The boxes are grouped by batches, but the sequential order in
+ * each batch is not guaranteed. For type of
+ * {@link OperandType::TENSOR_QUANT8_ASYMM}, the scale and zero
+ * point must be the same as input0.
+ * * 1: A tensor of the same {@link OperandType} as input1, of shape
+ * [num_output_rois, 4], specifying the coordinates of each output
+ * bounding box for each class, with format [x1, y1, x2, y2].
+ * The sequential order of the boxes corresponds with output0.
+ * For type of {@link OperandType::TENSOR_QUANT16_ASYMM}, the
+ * scale must be 0.125 and the zero point must be 0.
+ * * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
+ * [batches], specifying the number of output boxes for each image.
+ *
+ * Available since API level 29.
+ */
GENERATE_PROPOSALS = 52,
+
+ /**
+ * For input tensors x and y, computes x > y elementwise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: from 1
+ *
+ * This operation supports broadcasting.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same {@link OperandType} and dimensions compatible
+ * with input0.
+ *
+ * Outputs:
+ * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+ *
+ * Available since API level 29.
+ */
GREATER = 53,
+ /**
+ * For input tensors x and y, computes x >= y elementwise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: from 1
+ *
+ * This operation supports broadcasting.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same {@link OperandType} and dimensions compatible
+ * with input0.
+ *
+ * Outputs:
+ * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+ *
+ * Available since API level 29.
+ */
GREATER_EQUAL = 54,
+
+ /**
+ * Performs a grouped 2-D convolution operation.
+ *
+ * Given an input tensor of shape [batches, height, width, depth_in] and a
+ * filter tensor of shape [depth_out, filter_height, filter_width, depth_group]
+ * containing depth_out convolutional filters of depth depth_group, GROUPED_CONV
+ * applies a group of different filters to each input channel group, then
+ * concatenates the results together.
+ *
+ * Specifically, the input channels are divided into num_groups groups, each with
+ * depth depth_group, i.e. depth_in = num_groups * depth_group. The convolutional
+ * filters are also divided into num_groups groups, i.e. depth_out is divisible
+ * by num_groups. GROUPED_CONV applies each group of filters to the corresponding
+ * input channel group, and the result are concatenated together.
+ *
+ * The output dimensions are functions of the filter dimensions, stride, and
+ * padding.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[b, i, j, g * channel_multiplier + q] =
+ * sum_{di, dj, dk} (
+ * input[b, strides[1] * i + di, strides[2] * j + dj,
+ * g * depth_group + dk] *
+ * filter[g * channel_multiplier + q, di, dj, dk]
+ * ) + bias[channel]
+ *
+ * where channel_multiplier = depth_out / num_groups
+ *
+ * Supported tensor {@link OperandType} configurations:
+ * * 32 bit Floating point :
+ * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
+ *
+ * * 16 bit Floating point:
+ * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
+ *
+ * * Quantized:
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output.
+ * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
+ * * * input.scale * filter.scale).
+ *
+ * * Quantized with symetric per channel quantization for the filter:
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, and output.
+ * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
+ * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
+ * * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ *
+ * Both explicit padding and implicit padding are supported.
+ *
+ * Inputs (explicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+ * specifying the input, where depth_in = num_groups * depth_group.
+ * * 1: A 4-D tensor, of shape
+ * [depth_out, filter_height, filter_width, depth_group], specifying
+ * the filter, where depth_out must be divisible by num_groups. For
+ * tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
+ * the channel dimension must be set to 0.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+ * tensor of type {@link OperandType::TENSOR_FLOAT32} or
+ * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same
+ * type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
+ * of 0 and bias_scale == input_scale * filter_scale. For filter tensor
+ * of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
+ * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
+ * 0 and bias_scale of 0. The actual scale of each value 'i' is equal to
+ * bias_scale[i] = input_scale * filter_scale[i].
+ * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the left, in the ‘width’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the right, in the ‘width’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the top, in the ‘height’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the bottom, in the ‘height’ dimension.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 8: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 9: An {@link OperandType::INT32} scalar, specifying the number of
+ groups.
+ * * 10: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 11: An {@link OperandType::BOOL} scalar, set to true to specify
+ * NCHW data layout for input0 and output0. Set to false for NHWC.
+ *
+ * Inputs (implicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+ * specifying the input, where depth_in = num_groups * depth_group.
+ * * 1: A 4-D tensor, of shape
+ * [depth_out, filter_height, filter_width, depth_group], specifying
+ * the filter, where depth_out must be divisible by num_groups. For
+ * tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
+ * the channel dimension must be set to 0.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+ * tensor of type {@link OperandType::TENSOR_FLOAT32} or
+ * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same
+ * type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
+ * of 0 and bias_scale == input_scale * filter_scale. For filter tensor
+ * of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
+ * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
+ * 0 and bias_scale of 0. The actual scale of each value 'i' is equal to
+ * bias_scale[i] = input_scale * filter_scale[i].
+ * * 3: An {@link OperandType::INT32} scalar, specifying the implicit
+ * padding scheme, has to be one of the
+ * following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the number of
+ * groups.
+ * * 7: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 8: An {@link OperandType::BOOL} scalar, set to true to specify
+ * NCHW data layout for input0 and output0. Set to false for NHWC.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape
+ * [batches, out_height, out_width, depth_out]. For output tensor of
+ * {@link OperandType::TENSOR_QUANT8_ASYMM}, the following condition
+ * must be satisfied: output_scale > input_scale * filter_scale (for
+ * filter tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
+ * this condition must be true for all filter scales).
+ *
+ * Available since API level 29.
+ */
GROUPED_CONV_2D = 55,
+
+ /**
+ * Localize the maximum keypoints from heatmaps.
+ *
+ * This operation approximates the accurate maximum keypoint scores and
+ * indices after bicubic upscaling by using Taylor expansion up to the
+ * quadratic term.
+ *
+ * The bounding box is represented by its upper-left corner coordinate
+ * (x1,y1) and lower-right corner coordinate (x2,y2) in the original image.
+ * A valid bounding box should satisfy x1 <= x2 and y1 <= y2.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ *
+ * Inputs:
+ * * 0: A 4-D Tensor of shape
+ * [num_boxes, heatmap_size, heatmap_size, num_keypoints],
+ * specifying the heatmaps, the height and width of heatmaps should
+ * be the same, and must be greater than or equal to 2.
+ * * 1: A 2-D Tensor of shape [num_boxes, 4], specifying the bounding boxes,
+ * each with format [x1, y1, x2, y2]. For input0 of type
+ * {@link OperandType::TENSOR_QUANT8_ASYMM}, this tensor should
+ * be of {@link OperandType::TENSOR_QUANT16_ASYMM}, with zeroPoint
+ * of 0 and scale of 0.125.
+ * * 2: An {@link OperandType::BOOL} scalar, set to true to specify
+ * NCHW data layout for input0. Set to false for NHWC.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0, with shape
+ * [num_boxes, num_keypoints], specifying score of the keypoints.
+ * * 1: A tensor of the same {@link OperandType} as input1, with shape
+ * [num_boxes, num_keypoints, 2], specifying the location of
+ * the keypoints, the second dimension is organized as
+ * [keypoint_x, keypoint_y].
+ *
+ * Available since API level 29.
+ */
HEATMAP_MAX_KEYPOINT = 56,
+
+ /**
+ * Applies instance normalization to the input tensor.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[b, h, w, c] =
+ * (input[b, h, w, c] - mean[b, c]) * gamma /
+ * sqrt(var[b, c] + epsilon) + beta
+ *
+ * Where the mean and variance are computed across the spatial dimensions:
+ *
+ * mean[b, c] =
+ * sum_{h, w}(input[b, h, w, c]) / sum(1)
+ *
+ * var[b, c] =
+ * sum_{h, w}(pow(input[b, h, w, c] - mean[b, c], 2)) / sum(1)
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ *
+ * Inputs:
+ * * 0: An n-D tensor, specifying the tensor to be normalized.
+ * * 1: An {@link OperandType::FLOAT32} scalar, specifying gamma, the
+ * scale applied to the normalized tensor.
+ * * 2: An {@link OperandType::FLOAT32} scalar, specifying beta, the
+ * offset applied to the normalized tensor.
+ * * 3: An {@link OperandType::FLOAT32} scalar, specifying epsilon, the
+ * small value added to variance to avoid dividing by zero.
+ * * 4: An {@link OperandType::BOOL} scalar, set to true to specify
+ * NCHW data layout for input0 and output0. Set to false for NHWC.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} and same shape as input0.
+ *
+ * Available since API level 29.
+ */
INSTANCE_NORMALIZATION = 57,
+
+ /**
+ * For input tensors x and y, computes x < y elementwise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: from 1
+ *
+ * This operation supports broadcasting.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same {@link OperandType} and dimensions compatible
+ * with input0.
+ *
+ * Outputs:
+ * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+ *
+ * Available since API level 29.
+ */
LESS = 58,
+
+ /**
+ * For input tensors x and y, computes x <= y elementwise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: from 1
+ *
+ * This operation supports broadcasting.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same {@link OperandType} and dimensions compatible
+ * with input0.
+ *
+ * Outputs:
+ * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+ *
+ * Available since API level 29.
+ */
LESS_EQUAL = 59,
+
+ /**
+ * Computes natural logarithm of x element-wise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ *
+ * Available since API level 29.
+ */
LOG = 60,
+
+ /**
+ * Returns the truth value of x AND y element-wise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ *
+ * Supported tensor rank: from 1
+ *
+ * This operation supports broadcasting.
+ *
+ * Inputs:
+ * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+ * * 1: A tensor of {@link OperandType::TENSOR_BOOL8} and dimensions
+ * compatible with input0.
+ *
+ * Outputs:
+ * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+ *
+ * Available since API level 29.
+ */
LOGICAL_AND = 61,
+
+ /**
+ * Computes the truth value of NOT x element-wise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ *
+ * Available since API level 29.
+ */
LOGICAL_NOT = 62,
+
+ /**
+ * Returns the truth value of x OR y element-wise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ *
+ * Supported tensor rank: from 1
+ *
+ * This operation supports broadcasting.
+ *
+ * Inputs:
+ * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+ * * 1: A tensor of {@link OperandType::TENSOR_BOOL8} and dimensions
+ * compatible with input0.
+ *
+ * Outputs:
+ * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+ *
+ * Available since API level 29.
+ */
LOGICAL_OR = 63,
+
+ /**
+ * Computes the log softmax activations given logits.
+ *
+ * The output is calculated using this formula:
+ *
+ * output = logits * beta - log(reduce_sum(exp(logits * beta), axis))
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor specifying the input logits.
+ * * 1: An {@link OperandType::FLOAT32} scalar, specifying the positive
+ * scaling factor for the exponent, beta.
+ * * 2: An {@link OperandType::INT32} scalar specifying the axis to
+ * reduce across. Negative index is used to specify axis from the
+ * end (e.g. -1 for the last axis). Must be in the range [-n, n).
+ *
+ * Outputs:
+ * * 0: The output tensor of the same {@link OperandType} and shape as
+ * input0.
+ *
+ * Available since API level 29.
+ */
LOG_SOFTMAX = 64,
+
+ /**
+ * Returns the element-wise maximum of two tensors.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same {@link OperandType} and compatible dimensions
+ * with input0.
+ *
+ * Outputs:
+ * * 0: The sum, a tensor of the same {@link OperandType} as input0.
+ *
+ * Available since API level 29.
+ */
MAXIMUM = 65,
+
+ /**
+ * Returns the element-wise minimum of two tensors.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same {@link OperandType} and compatible dimensions
+ * with input0.
+ *
+ * Outputs:
+ * * 0: The sum, a tensor of the same {@link OperandType} as input0.
+ *
+ * Available since API level 29.
+ */
MINIMUM = 66,
+
+ /**
+ * Computes numerical negative value element-wise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ *
+ * Available since API level 29.
+ */
NEG = 67,
+
+ /**
+ * For input tensors x and y, computes x != y elementwise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: from 1
+ *
+ * This operation supports broadcasting.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same {@link OperandType} and dimensions compatible
+ * with input0.
+ *
+ * Outputs:
+ * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+ *
+ * Available since API level 29.
+ */
NOT_EQUAL = 68,
+
+ /**
+ * Pads a tensor with the given constant value according to the specified
+ * paddings.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor, specifying the tensor to be padded.
+ * * 1: A 2-D Tensor of {@link OperandType::TENSOR_INT32}, the paddings
+ * for each spatial dimension of the input tensor. The shape of the
+ * tensor must be {rank(input0), 2}.
+ * padding[i, 0] specifies the number of elements to be padded in the
+ * front of dimension i.
+ * padding[i, 1] specifies the number of elements to be padded after
+ * the end of dimension i.
+ * * 2: An scalar specifying the value to use for padding input0.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT32}, the
+ * pad value should be of {@link OperandType::FLOAT32}.
+ * For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the pad value should be of {@link OperandType::INT32}. The
+ * scale and zeroPoint are assumed to be the same as in input0.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0. The
+ * output tensor has the same rank as input0, and each
+ * dimension of the output tensor has the same size as the
+ * corresponding dimension of the input tensor plus the size
+ * of the padding:
+ * output0.dimension[i] =
+ * padding[i, 0] + input0.dimension[i] + padding[i, 1]
+ *
+ * Available since API level 29.
+ */
PAD_V2 = 69,
+
+ /**
+ * Computes the power of one value to another.
+ *
+ * Given a tensor base and a tensor exponent, this operation computes
+ * base^exponent elementwise.
+ *
+ * This operations supports broadcasting. The size of the output is the
+ * maximum size along each dimension of the input operands. It starts with
+ * the trailing dimensions, and works its way forward.
+ *
+ * For example:
+ * base.dimension = {4, 1, 2}
+ * exponent.dimension = {5, 4, 3, 1}
+ * output.dimension = {5, 4, 3, 2}
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: A tensor specifying the base.
+ * * 1: A tensor specifying the exponent.
+ *
+ * Outputs:
+ * * 0: An output tensor.
+ *
+ * Available since API level 29.
+ */
POW = 70,
+
+ /**
+ * Parametric Rectified Linear Unit.
+ *
+ * It follows: f(x) = alpha * x for x < 0, f(x) = x for x >= 0, where alpha
+ * is a learned array with the same {@link OperandType} and compatible
+ * dimensions as input x.
+ *
+ * Two dimensions are compatible when:
+ * 1. they are equal, or
+ * 2. one of them is 1
+ *
+ * The size of the output is the maximum size along each dimension of the
+ * input operands. It starts with the trailing dimensions, and works its way
+ * forward.
+ *
+ * Example:
+ * input.dimension = {4, 1, 2}
+ * alpha.dimension = {5, 4, 3, 1}
+ * output.dimension = {5, 4, 3, 2}
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input.
+ * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
+ * as input0, specifying the alpha.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ *
+ * Available since API level 29.
+ */
PRELU = 71,
+
+ /**
+ * Quantizes the input tensor.
+ *
+ * The formula is:
+ *
+ * output = max(0, min(255, round(input / scale) + zeroPoint)
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0, but with
+ * {@link OperandType::TENSOR_QUANT8_ASYMM}.
+ *
+ * Available since API level 29.
+ */
QUANTIZE = 72,
+
+ /**
+ * A version of quantized LSTM, using 16 bit quantization for internal
+ * state.
+ *
+ * There is no projection layer, so cell state size is equal to the output
+ * size.
+ *
+ * Inputs:
+ * * 0: A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [numBatches, inputSize] specifying the input to the LSTM
+ * cell. Tensor is quantized with a fixed quantization range of
+ * [-1, 127/128] (scale = 1/128, zeroPoint = 128).
+ * * 1: The input-to-input weights.
+ * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [outputSize, inputSize] specifying input-to-input part of
+ * weights for fully-connected layer inside the LSTM cell.
+ * Quantization zero point and scale must be the same across all the
+ * weights.
+ * * 2: The input-to-forget weights.
+ * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [outputSize, inputSize] specifying input-to-forget part of
+ * weights for fully-connected layer inside the LSTM cell.
+ * Quantization zero point and scale must be the same across all the
+ * weights.
+ * * 3: The input-to-cell weights.
+ * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [outputSize, inputSize] specifying input-to-cell part of
+ * weights for fully-connected layer inside the LSTM cell.
+ * Quantization zero point and scale must be the same across all the
+ * weights.
+ * * 4: The input-to-output weights.
+ * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [outputSize, inputSize] specifying input-to-output part of
+ * weights for fully-connected layer inside the LSTM cell.
+ * Quantization zero point and scale must be the same across all the
+ * weights.
+ * * 5: The recurrent-to-input weights.
+ * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [outputSize, inputSize] specifying recurrent-to-input part
+ * of weights for fully-connected layer inside the LSTM cell.
+ * Quantization zero point and scale must be the same across all the
+ * weights.
+ * * 6: The recurrent-to-forget weights.
+ * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [outputSize, inputSize] specifying recurrent-to-forget
+ * part of weights for fully-connected layer inside the LSTM cell.
+ * Quantization zero point and scale must be the same across all the
+ * weights.
+ * * 7: The recurrent-to-cell weights.
+ * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [outputSize, inputSize] specifying recurrent-to-cell part
+ * of weights for fully-connected layer inside the LSTM cell.
+ * Quantization zero point and scale must be the same across all the
+ * weights.
+ * * 8: The recurrent-to-output weights.
+ * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [outputSize, inputSize] specifying recurrent-to-output
+ * part of weights for fully-connected layer inside the LSTM cell.
+ * Quantization zero point and scale must be the same across all the
+ * weights.
+ * * 9: The input gate bias.
+ * A 1-D tensor of type {@link OperandType::TENSOR_INT32} and shape
+ * [outputSize] specifying the bias for the fully-connected layer
+ * inside the LSTM cell. Bias is quantized with scale being a product
+ * of input and weights scales and zeroPoint equal to 0.
+ * * 10:The forget gate bias.
+ * A 1-D tensor of type {@link OperandType::TENSOR_INT32} and shape
+ * [outputSize] specifying the bias for the fully-connected layer
+ * inside the LSTM cell. Bias is quantized with scale being a product
+ * of input and weights scales and zeroPoint equal to 0.
+ * * 11:The cell bias.
+ * A 1-D tensor of type {@link OperandType::TENSOR_INT32} and shape
+ * [outputSize] specifying the bias for the fully-connected layer
+ * inside the LSTM cell. Bias is quantized with scale being a product
+ * of input and weights scales and zeroPoint equal to 0.
+ * * 12:The output gate bias.
+ * A 1-D tensor of type {@link OperandType::TENSOR_INT32} and shape
+ * [outputSize] specifying the bias for the fully-connected layer
+ * inside the LSTM cell. Bias is quantized with scale being a product
+ * of input and weights scales and zeroPoint equal to 0.
+ * * 13: A 2-D tensor of type {@link OperandType::TENSOR_QUANT16_SYMM}
+ * and shape [numBatches, outputSize] specifying the cell state from the
+ * previous time step of the LSTM cell. It is quantized using a
+ * quantization range of [-2^4, 2^4 * 32767/32768] (scale = 2^4 /
+ * 32768, zeroPoint = 0).
+ * * 14: A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [numBathes, outputSize] specifying the output of the LSTM
+ * cell from previous time-step. Tensor is quantized with a fixed
+ * quantization range of [-1, 127/128] (scale = 1/128, zeroPoint =
+ * 128).
+ *
+ *
+ * Outputs:
+ * * 0: A 2-D tensor of type {@link OperandType::TENSOR_QUANT16_SYMM}
+ * and shape [numBatches, outputSize] which contains a cell state from
+ * the current time step. Tensor is quantized using a quantization
+ * range of [-2^4, 2^4 * 32767/32768] (scale = 2^4 / 32768, zeroPoint =
+ * 0).
+ * * 1: A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [numBathes, outputSize] which contains the output value.
+ * Tensor is quantized with a fixed quantization range of [-1, 127/128]
+ * (scale = 1/128, zeroPoint = 128).
+ */
QUANTIZED_16BIT_LSTM = 73,
+
+ /**
+ * Draws samples from a multinomial distribution.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Inputs:
+ * * 0: A 2-D tensor with shape [batches, classes], specifying the
+ * unnormalized log-probabilities for all classes.
+ * * 1: A scalar {@link OperandType::INT32}, specifying the number of
+ * independent samples to draw for each row slice.
+ * * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor with shape [2],
+ * specifying seeds used to initialize the random distribution.
+ * Outputs:
+ * * 0: A 2-D {@link OperandType::TENSOR_INT32} tensor with shape
+ * [batches, samples], containing the drawn samples.
+ *
+ * Available since API level 29.
+ */
RANDOM_MULTINOMIAL = 74,
+
+ /**
+ * Reduces a tensor by computing the "logical and" of elements along given
+ * dimensions.
+ *
+ * If keep_dims is true, the reduced dimensions are
+ * retained with length 1. Otherwise, the rank of the tensor is reduced by
+ * 1 for each entry in dimensions.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor.
+ * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions
+ * to reduce. Dimension values must be in the range [-n, n).
+ * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true,
+ * retains reduced dimensions with length 1.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ *
+ * Available since API level 29.
+ */
REDUCE_ALL = 75,
+
+ /**
+ * Reduces a tensor by computing the "logical or" of elements along given
+ * dimensions.
+ *
+ * If keep_dims is true, the reduced dimensions are
+ * retained with length 1. Otherwise, the rank of the tensor is reduced by
+ * 1 for each entry in dimensions.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor.
+ * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions
+ * to reduce. Dimension values must be in the range [-n, n).
+ * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true,
+ * retains reduced dimensions with length 1.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ *
+ * Available since API level 29.
+ */
REDUCE_ANY = 76,
+
+ /**
+ * Reduces a tensor by computing the maximum of elements along given
+ * dimensions.
+ *
+ * If keep_dims is true, the reduced dimensions are
+ * retained with length 1. Otherwise, the rank of the tensor is reduced by
+ * 1 for each entry in dimensions.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor.
+ * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions
+ * to reduce. Dimension values must be in the range [-n, n).
+ * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true,
+ * retains reduced dimensions with length 1.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ *
+ * Available since API level 29.
+ */
REDUCE_MAX = 77,
+
+ /**
+ * Reduces a tensor by computing the minimum of elements along given
+ * dimensions.
+ *
+ * If keep_dims is true, the reduced dimensions are
+ * retained with length 1. Otherwise, the rank of the tensor is reduced by
+ * 1 for each entry in dimensions.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor.
+ * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions
+ * to reduce. Dimension values must be in the range [-n, n).
+ * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true,
+ * retains reduced dimensions with length 1.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ *
+ * Available since API level 29.
+ */
REDUCE_MIN = 78,
+
+ /**
+ * Reduces a tensor by multiplying elements along given dimensions.
+ *
+ * If keep_dims is true, the reduced dimensions are
+ * retained with length 1. Otherwise, the rank of the tensor is reduced by
+ * 1 for each entry in dimensions.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor.
+ * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions
+ * to reduce. Dimension values must be in the range [-n, n).
+ * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true,
+ * retains reduced dimensions with length 1.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ *
+ * Available since API level 29.
+ */
REDUCE_PROD = 79,
+
+ /**
+ * Reduces a tensor by summing elements along given dimensions.
+ *
+ * If keep_dims is true, the reduced dimensions are
+ * retained with length 1. Otherwise, the rank of the tensor is reduced by
+ * 1 for each entry in dimensions.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor.
+ * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions
+ * to reduce. Dimension values must be in the range [-n, n).
+ * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true,
+ * retains reduced dimensions with length 1.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ *
+ * Available since API level 29.
+ */
REDUCE_SUM = 80,
+
+ /**
+ * Select and scale the feature map of each region of interest to a unified
+ * output size by average pooling sampling points from bilinear interpolation.
+ *
+ * The region of interest is represented by its upper-left corner coordinate
+ * (x1,y1) and lower-right corner coordinate (x2,y2) in the original image.
+ * A spatial scaling factor is applied to map into feature map coordinate.
+ * A valid region of interest should satisfy x1 <= x2 and y1 <= y2.
+ *
+ * No rounding is applied in this operation. The sampling points are unified
+ * distributed in the pooling bin and their values are calculated by bilinear
+ * interpolation.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ *
+ * Inputs:
+ * * 0: A 4-D tensor, specifying the feature map.
+ * * 1: A 2-D Tensor of shape [num_rois, 4], specifying the locations of
+ * the regions of interest, each line with format [x1, y1, x2, y2].
+ * For input0 of type {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * this tensor should be of {@link OperandType::TENSOR_QUANT16_ASYMM},
+ * with zeroPoint of 0 and scale of 0.125.
+ * * 2: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
+ * [batches], specifying the number of output boxes for each batch.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the output
+ * height of the output tensor.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the output
+ * width of the output tensor.
+ * * 5: An {@link OperandType::FLOAT32} scalar, specifying the ratio
+ * from the height of original image to the height of feature map.
+ * * 6: An {@link OperandType::FLOAT32} scalar, specifying the ratio
+ * from the width of original image to the width of feature map.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the number of
+ * sampling points in height dimension used to compute the output.
+ * Set to 0 for adaptive value of ceil(roi_height/out_height).
+ * * 8: An {@link OperandType::INT32} scalar, specifying the number of
+ * sampling points in width dimension used to compute the output.
+ * Set to 0 for adaptive value of ceil(roi_width/out_width).
+ * * 9: An {@link OperandType::BOOL} scalar, set to true to specify
+ * NCHW data layout for input0 and output0. Set to false for NHWC.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0. The output
+ * shape is [num_rois, out_height, out_width, depth].
+ *
+ * Available since API level 29.
+ */
ROI_ALIGN = 81,
+
+ /**
+ * Select and scale the feature map of each region of interest to a unified
+ * output size by max-pooling.
+ *
+ * The region of interest is represented by its upper-left corner coordinate
+ * (x1,y1) and lower-right corner coordinate (x2,y2) in the original image.
+ * A spatial scaling factor is applied to map into feature map coordinate.
+ * A valid region of interest should satisfy x1 <= x2 and y1 <= y2.
+ *
+ * Rounding is applied in this operation to ensure integer boundary for
+ * regions of interest and pooling bins.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ *
+ * Inputs:
+ * * 0: A 4-D tensor, specifying the feature map.
+ * * 1: A 2-D Tensor of shape [num_rois, 4], specifying the locations of
+ * the regions of interest, each line with format [x1, y1, x2, y2].
+ * For input0 of type {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * this tensor should be of {@link OperandType::TENSOR_QUANT16_ASYMM},
+ * with zeroPoint of 0 and scale of 0.125.
+ * * 2: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
+ * [batches], specifying the number of output boxes for each batch.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the output
+ * height of the output tensor.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the output
+ * width of the output tensor.
+ * * 5: An {@link OperandType::FLOAT32} scalar, specifying the ratio
+ * from the height of original image to the height of feature map.
+ * * 6: An {@link OperandType::FLOAT32} scalar, specifying the ratio
+ * from the width of original image to the width of feature map.
+ * * 7: An {@link OperandType::BOOL} scalar, set to true to specify
+ * NCHW data layout for input0 and output0. Set to false for NHWC.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0. The output
+ * shape is [num_rois, out_height, out_width, depth].
+ *
+ * Available since API level 29.
+ */
ROI_POOLING = 82,
+
+ /**
+ * Computes reciprocal of square root of x element-wise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ *
+ * Available since API level 29.
+ */
RSQRT = 83,
+
+ /**
+ * Using a tensor of booleans c and input tensors x and y select values
+ * elementwise from both input tensors:
+ *
+ * O[i] = C[i] ? x[i] : y[i].
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: A tensor of type {@link OperandType::TENSOR_BOOL8} acting as a
+ * mask that chooses, based on the value at each element, whether the
+ * corresponding element in the output should be taken from input1 (if
+ * true) or input2 (if false).
+ * * 1: An input tensor of the same shape as input0.
+ * * 2: An input tensor of the same shape and type as input1.
+ *
+ * Outputs:
+ * * 0: A tensor of the same type and shape as input1 and input2.
+ *
+ */
SELECT = 84,
+
+ /**
+ * Computes sin of x element-wise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ *
+ * Available since API level 29.
+ */
SIN = 85,
+
+ /**
+ * Extracts a slice of specified size from the input tensor starting at a
+ * specified location.
+ *
+ * The starting location is specified as a 1-D tensor containing offsets
+ * for each dimension. The size is specified as a 1-D tensor containing
+ * either size of a slice along corresponding dimension or -1. In the latter
+ * case, all the remaining elements in dimension are included in the slice.
+ * Slice size in each dimension cannot be zero.
+ *
+ * A sum of begin offset and a size of a slice must not exceed size of a
+ * corresponding dimension.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: An n-D tensor to take slice from.
+ * * 1: A 1-D tensor of type {@link OperandType::TENSOR_INT32} specifying
+ * the beginning indices of the slice in each dimension.
+ * * 2: A 1-D tensor of type {@link OperandType::TENSOR_INT32} specifying
+ * the size of the slice in each dimension.
+ *
+ * Outputs:
+ * * 0: An n-D tensor of the same type as the input containing the slice.
+ *
+ * Available since API level 29.
+ */
SLICE = 86,
+
+ /**
+ * Splits a tensor along a given axis into num_splits subtensors.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: An n-D tensor to split.
+ * * 1: An {@link OperandType::INT32} scalar specifying the axis along
+ * which to split.
+ * * 2: An {@link OperandType::INT32} scalar indicating the number of
+ * splits along given axis. Must evenly divide axis size.
+ *
+ * Outputs:
+ * * 0 ~ (num_splits - 1): Resulting subtensors.
+ *
+ * Available since API level 29.
+ */
SPLIT = 87,
+
+ /**
+ * Computes square root of x element-wise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ *
+ * Available since API level 29.
+ */
SQRT = 88,
+
+ /**
+ * Constructs a tensor by tiling a given tensor.
+ *
+ * This operation creates a new tensor by replicating `input` `multiples`
+ * times. The output tensor's i-th dimension has `input.dims(i) * multiples[i]`
+ * elements, and the values of `input` are replicated `multiples[i]` times
+ * along the i-th dimension.
+ * For example, tiling `[a b c d]` by `[2]` produces `[a b c d a b c d]`.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: input, an n-D tensor specifying the input.
+ * * 1: multiples, a 1-D tensor of {@link OperandType::TENSOR_INT32}.
+ * The length of multiples must be n.
+ *
+ * Outputs:
+ * * 0: A tiled tensor of the same {@link OperandType} and rank as `input`.
+ *
+ * Available since API level 29.
+ */
TILE = 89,
+
+ /**
+ * Finds values and indices of the k largest entries for the last dimension.
+ *
+ * Resulting values in each dimensions are sorted in descending order. If
+ * two values are equal, the one with larger index appears first.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: input, an n-D tensor specifying the input.
+ * * 1: k, an {@link OperandType::INT32} scalar, specifying the number of
+ * top elements to look for along the last dimension.
+ *
+ * Outputs:
+ * * 0: An n-D tensor of the same type as the input, containing the k
+ * largest elements along each last dimensional slice.
+ * * 1: An n-D tensor of type {@link OperandType::TENSOR_INT32}
+ * containing the indices of values within the last dimension of input.
+ *
+ * Available since API level 29.
+ */
TOPK_V2 = 90,
+
+ /**
+ * Performs the tranpose of 2-D convolution operation.
+ *
+ * This operation is sometimes called "deconvolution" after Deconvolutional
+ * Networks, but is actually the transpose (gradient) of
+ * {@link OperandType::CONV_2D} rather than an actual deconvolution.
+ *
+ * The output dimensions are functions of the filter dimensions, stride, and
+ * padding.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ *
+ * Both explicit padding and implicit padding are supported.
+ *
+ * Inputs (explicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+ * specifying the input.
+ * * 1: A 4-D tensor, of shape
+ * [depth_out, filter_height, filter_width, depth_in], specifying the
+ * filter.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+ * tensor of type {@link OperandType::TENSOR_FLOAT32} or
+ * {@link OperandType::TENSOR_FLOAT16}, the bias should be of the
+ * same type. For input tensor of type
+ * {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be
+ * of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
+ * bias_scale == input_scale * filter_scale.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the left, in the ‘width’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the right, in the ‘width’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the top, in the ‘height’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the bottom, in the ‘height’ dimension.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 8: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 9: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 10: An {@link OperandType::BOOL} scalar, set to true to specify
+ * NCHW data layout for input0 and output0. Set to false for NHWC.
+ *
+ * Inputs (implicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+ * specifying the input.
+ * * 1: A 4-D tensor, of shape
+ * [depth_out, filter_height, filter_width, depth_in], specifying the
+ * filter.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+ * tensor of type {@link OperandType::TENSOR_FLOAT32} or
+ * {@link OperandType::TENSOR_FLOAT16}, the bias should be of the
+ * same type. For input tensor of type
+ * {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be
+ * of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
+ * bias_scale == input_scale * filter_scale.
+ * * 3: An {@link OperandType::TENSOR_INT32} tensor, specifying the output
+ * tensor shape.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the implicit
+ * padding scheme, has to be one of the
+ * following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 7: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 8: An {@link OperandType::BOOL} scalar, set to true to specify
+ * NCHW data layout for input0 and output0. Set to false for NHWC.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape
+ * [batches, out_height, out_width, depth_out]. For output tensor of
+ * {@link OperandType::TENSOR_QUANT8_ASYMM}, the following condition
+ * must be satisfied: output_scale > input_scale * filter_scale.
+ *
+ * Available since API level 29.
+ */
TRANSPOSE_CONV_2D = 91,
+
+ /**
+ * A recurrent neural network specified by an LSTM cell.
+ *
+ * Performs (fully) dynamic unrolling of input.
+ *
+ * This Op unrolls the input along the time dimension, and implements the
+ * following operation for each element in the sequence
+ * s = 1...sequence_length:
+ * outputs[s] = projection(state = activation(LSTMOp(inputs[s])))
+ *
+ * Where LSTMOp is the LSTM op as in {@link OperandType::LSTM},
+ * the "projection" is an optional projection layer from state and output
+ * and the “activation” is the function passed as the
+ * “fused_activation_function” argument (if not “NONE”).
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: 3, either time-major or batch-major.
+ *
+ * All input and output tensors must be of the same type.
+ *
+ * Inputs:
+ * * 0: The input (\f$x_t\f$).
+ * A 3-D tensor of shape:
+ * If time-major: [max_time, batch_size, output_size]
+ * If batch-major: [batch_size, max_time, output_size]
+ * where “max_size” is the number of timesteps (sequence length),
+ * “batch_size” corresponds to the batching dimension, and
+ * “input_size” is the size of the input.
+ * * 1: The input-to-input weights (\f$W_{xi}\f$). Optional.
+ * A 2-D tensor of shape [num_units, input_size], where “num_units”
+ * corresponds to the number of cell units.
+ * * 2: The input-to-forget weights (\f$W_{xf}\f$).
+ * A 2-D tensor of shape [num_units, input_size].
+ * * 3: The input-to-cell weights (\f$W_{xc}\f$).
+ * A 2-D tensor of shape [num_units, input_size].
+ * * 4: The input-to-output weights (\f$W_{xo}\f$).
+ * A 2-D tensor of shape [num_units, input_size].
+ * * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional.
+ * A 2-D tensor of shape [num_units, output_size], where “output_size”
+ * corresponds to either the number of cell units (i.e., “num_units”),
+ * or the second dimension of the “projection_weights”, if defined.
+ * * 6: The recurrent-to-forget weights (\f$W_{hf}\f$).
+ * A 2-D tensor of shape [num_units, output_size].
+ * * 7: The recurrent-to-cell weights (\f$W_{hc}\f$).
+ * A 2-D tensor of shape [num_units, output_size].
+ * * 8: The recurrent-to-output weights (\f$W_{ho}\f$).
+ * A 2-D tensor of shape [num_units, output_size].
+ * * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 11:The cell-to-output weights (\f$W_{co}\f$). Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 12:The input gate bias (\f$b_i\f$). Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 13:The forget gate bias (\f$b_f\f$).
+ * A 1-D tensor of shape [num_units].
+ * * 14:The cell bias (\f$b_c\f$).
+ * A 1-D tensor of shape [num_units].
+ * * 15:The output gate bias (\f$b_o\f$).
+ * A 1-D tensor of shape [num_units].
+ * * 16:The projection weights (\f$W_{proj}\f$). Optional.
+ * A 2-D tensor of shape [output_size, num_units].
+ * * 17:The projection bias (\f$b_{proj}\f$). Optional.
+ * A 1-D tensor of shape [output_size].
+ * * 18:The output state (in) (\f$h_{t-1}\f$).
+ * A 2-D tensor of shape [batch_size, output_size].
+ * * 19:The cell state (in) (\f$C_{t-1}\f$).
+ * A 2-D tensor of shape [batch_size, num_units].
+ * * 20:The activation function (\f$g\f$).
+ * A value indicating the activation function:
+ * <ul>
+ * <li>0: None;
+ * <li>1: Relu;
+ * <li>3: Relu6;
+ * <li>4: Tanh;
+ * <li>6: Sigmoid.
+ * </ul>
+ * * 21:The clipping threshold (\f$t_{cell}\f$) for the cell state, such
+ * that values are bound within [-cell_clip, cell_clip]. If set to 0.0
+ * then clipping is disabled.
+ * * 22:The clipping threshold (\f$t_{proj}\f$) for the output from the
+ * projection layer, such that values are bound within
+ * [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
+ * * 23:Time-major if true, batch-major if false.
+ * * 24:The input layer normalization weights.
+ * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+ * to activation at input gate.
+ * * 25:The forget layer normalization weights.
+ * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+ * to activation at forget gate.
+ * * 26:The cell layer normalization weights.
+ * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+ * to activation at cell gate.
+ * * 27:The output layer normalization weights.
+ * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+ * to activation at output gate.
+ *
+ * Outputs:
+ * * 0: The output (\f$o_t\f$).
+ * A 3-D tensor of shape:
+ * If time-major: [max_time, batch_size, output_size]
+ * If batch-major: [batch_size, max_time, output_size]
+ *
+ * Available since API level 29.
+ */
UNIDIRECTIONAL_SEQUENCE_LSTM = 92,
+
+ /**
+ * A recurrent neural network layer that applies a basic RNN cell to a
+ * sequence of inputs.
+ *
+ * This layer unrolls the input along the sequence dimension, and implements
+ * the following operation
+ * for each element in the sequence s = 1...sequence_length:
+ * outputs[s] = state = activation(inputs[s] * input_weights’ + state *
+ * recurrent_weights’ + bias)
+ *
+ * Where:
+ * * “input_weights” is a weight matrix that multiplies the inputs;
+ * * “recurrent_weights” is a weight matrix that multiplies the current
+ * “state” which itself is the output from the previous time step
+ * computation;
+ * * “bias” is a bias vector (added to each output vector in the batch);
+ * * “activation” is the function passed as the “fused_activation_function”
+ * argument (if not “NONE”).
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * The input tensors must all be the same type.
+ *
+ * Inputs:
+ * * 0: input.
+ * A 3-D tensor. The shape is defined by the input 6 (timeMajor). If
+ * it is set to 1, then the input has a shape [maxTime, batchSize,
+ * inputSize], otherwise the input has a shape [batchSize, maxTime,
+ * inputSize].
+ * * 1: weights.
+ * A 2-D tensor of shape [numUnits, inputSize].
+ * * 2: recurrent_weights.
+ * A 2-D tensor of shape [numUnits, numUnits].
+ * * 3: bias.
+ * A 1-D tensor of shape [numUnits].
+ * * 4: hidden state
+ * A 2-D tensor of shape [batchSize, numUnits]. Specifies a hidden
+ * state input for the first time step of the computation.
+ * * 5: fusedActivationFunction.
+ * A {@link FusedActivationFunc} value indicating the activation function. If
+ * “NONE” is specified then it results in a linear activation.
+ * * 6: timeMajor
+ * An {@link OperandType::INT32} scalar specifying the shape format
+ * of input and output tensors. Must be set to either 0 or 1.
+ * Outputs:
+ * * 0: output.
+ * A 3-D tensor. The shape is defined by the input 6 (timeMajor). If
+ * it is set to 1, then the output has a shape [maxTime, batchSize,
+ * numUnits], otherwise the output has a shape [batchSize, maxTime,
+ * numUnits].
+ *
+ * Available since API level 29.
+ */
UNIDIRECTIONAL_SEQUENCE_RNN = 93,
+ /**
+ * DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
+ * OEM operation and data types.
+ *
+ * This operation is OEM specific. It should only be used for OEM
+ * applications.
+ */
+ OEM_OPERATION = @1.1::OperationType:OEM_OPERATION,
/* ADDING A NEW FUNDAMENTAL OPERATION REQUIRES UPDATING THE VALUE OF
* OperationTypeRange::FUNDAMENTAL_MAX.
*/