/*
 * Copyright (C) 2017 The Android Open Source Project
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

/**
 * @addtogroup NeuralNetworks
 * @{
 */

/**
 * @file NeuralNetworks.h
 */

#ifndef ANDROID_ML_NN_RUNTIME_NEURAL_NETWORKS_H
#define ANDROID_ML_NN_RUNTIME_NEURAL_NETWORKS_H

/******************************************************************
 *
 * IMPORTANT NOTICE:
 *
 *   This file is part of Android's set of stable system headers
 *   exposed by the Android NDK (Native Development Kit).
 *
 *   Third-party source AND binary code relies on the definitions
 *   here to be FROZEN ON ALL UPCOMING PLATFORM RELEASES.
 *
 *   - DO NOT MODIFY ENUMS (EXCEPT IF YOU ADD NEW 32-BIT VALUES)
 *   - DO NOT MODIFY CONSTANTS OR FUNCTIONAL MACROS
 *   - DO NOT CHANGE THE SIGNATURE OF FUNCTIONS IN ANY WAY
 *   - DO NOT CHANGE THE LAYOUT OR SIZE OF STRUCTURES
 */

#if __ANDROID_API__ >= __ANDROID_API_O_MR1__

#include <stddef.h>
#include <stdint.h>
#include <sys/cdefs.h>

__BEGIN_DECLS

/**
 * Operand types.
 *
 * The type of operands that can be added to a model.
 *
 * Although we define many types, most operators accept just a few
 * types. Most used are {@link ANEURALNETWORKS_TENSOR_FLOAT32},
 * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM},
 * and {@link ANEURALNETWORKS_INT32}.
 */
typedef enum {
    /** The following entries are used to declare scalars. */

    /** A 32 bit floating point scalar value. */
    ANEURALNETWORKS_FLOAT32 = 0,
    /** A signed 32 bit integer scalar value. */
    ANEURALNETWORKS_INT32 = 1,
    /** An unsigned 32 bit integer scalar value. */
    ANEURALNETWORKS_UINT32 = 2,

    /** The following entries are used to declare tensors. */

    /** A tensor of 32 bit floating point values. */
    ANEURALNETWORKS_TENSOR_FLOAT32 = 3,
    /** A tensor of 32 bit integer values. */
    ANEURALNETWORKS_TENSOR_INT32 = 4,
    /** A tensor of 8 bit integers that represent real numbers.
     *
     * Attached to this tensor are two numbers that can be used to convert
     * the 8 bit integer to the real value and vice versa.  These two numbers are:
     * - scale: a 32 bit floating point value greater than zero.
     * - zeroPoint: an 32 bit integer, in range [0, 255].
     *
     * The formula is:
     * real_value = (integer_value - zeroPoint) * scale.
     */
    ANEURALNETWORKS_TENSOR_QUANT8_ASYMM = 5,
} OperandCode;

/**
 * Operation types.
 *
 * The type of operations that can be added to a model.
 */
typedef enum {
    /** Adds two tensors, element-wise.
     *
     * Takes two input tensors of identical type 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 types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
     *
     * Supported tensor rank: up to 4
     *
     * Inputs:
     * * 0: A tensor.
     * * 1: A tensor of the same type, and compatible dimensions as input0.
     * * 2: An INT32 value, and has to be one of the {@link FuseCode} values.
     *      Specifies the activation to invoke on the result of each addition.
     *
     * Outputs:
     * * 0: The sum, a tensor of the same type as input0.
     */
    ANEURALNETWORKS_ADD = 0,

    /** 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[batch, row, col, channel] =
     *         sum_{i, j}(input[batch, row + i, col + j, channel]) / sum(1)
     *
     * Supported tensor types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
     *
     * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width, and Channels)
     * data layout.
     *
     * 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 INT32 value, specifying the padding on the left, in the ‘width’ dimension.
     * * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
     * * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
     * * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
     * * 5: An INT32 value, specifying the stride when walking through input
     *      in the ‘width’ dimension.
     * * 6: An INT32 value, specifying the stride when walking through input
     *      in the ‘height’ dimension.
     * * 7: An INT32 value, specifying the filter width.
     * * 8: An INT32 value, specifying the filter height.
     * * 9: An INT32 value, and has to be one of the {@link FuseCode} values.
     *      Specifies the activation to invoke on the result of each addition.
     *
     * Inputs (implicit padding):
     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
     * * 1: An INT32 value, specifying the implicit padding scheme, has to be one of the
     *      {@link PaddingCode} values.
     * * 2: An INT32 value, specifying the stride when walking through input
     *      in the ‘width’ dimension.
     * * 3: An INT32 value, specifying the stride when walking through input
     *      in the ‘height’ dimension.
     * * 4: An INT32 value, specifying the filter width.
     * * 5: An INT32 value, specifying the filter height.
     * * 6: An INT32 value, and has to be one of the {@link FuseCode} values.
     *      Specifies the activation to invoke on the result of each addition.
     *
     * Outputs:
     * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
     */
    ANEURALNETWORKS_AVERAGE_POOL_2D = 1,

    /** Concatenates the input tensors along the given dimension.
     *
     * The input tensors must have identical type and the same dimensions except the
     * dimension along the concatenation axis.
     *
     * Supported tensor types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
     *
     * Supported tensor rank: up to 4
     *
     * Inputs:
     * * 0 ~ n-1: The list of n input tensors, of shape [D0, D1, ..., Daxis(i), ..., Dm].
     *            For inputs of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, all
     *            input tensors must have the same scale and zeroPoint.
     * * n: An INT32 value, specifying the concatenation axis.
     *
     * Outputs:
     * * 0: The output, a tensor of the same type as the input tensors.
     *      The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
     */
    ANEURALNETWORKS_CONCATENATION = 2,

    /** 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[batch, row, col, channel] =
     *         sum_{i, j} (
     *             input[batch, row + i, col + j, k] *
     *             filter[channel, row + i, col + j, k] +
     *             bias[channel]
     *         )
     *
     * Supported tensor types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
     *
     * Supported tensor rank: 4, with "NHWC" data layout.
     *
     * 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 {@link ANEURALNETWORKS_TENSOR_FLOAT32} type, the bias should
     *      also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
     *      For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias
     *      should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and
     *      bias_scale == input_scale * filter_scale.
     * * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
     * * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
     * * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
     * * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
     * * 7: An INT32 value, specifying the stride when walking through input
     *      in the ‘width’ dimension.
     * * 8: An INT32 value, specifying the stride when walking through input
     *      in the ‘height’ dimension.
     * * 9: An INT32 value, and has to be one of the {@link FuseCode} values.
     *      Specifies the activation to invoke on the result of each addition.
     *
     * 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 {@link ANEURALNETWORKS_TENSOR_FLOAT32} type, the bias should
     *      also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
     *      For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias
     *      should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and
     *      bias_scale == input_scale * filter_scale.
     * * 3: An INT32 value, specifying the implicit padding scheme, has to be one of the
     *      {@link PaddingCode} values.
     * * 4: An INT32 value, specifying the stride when walking through input
     *      in the ‘width’ dimension.
     * * 5: An INT32 value, specifying the stride when walking through input
     *      in the ‘height’ dimension.
     * * 6: An INT32 value, and has to be one of the {@link FuseCode} values.
     *      Specifies the activation to invoke on the result of each addition.
     *
     * Outputs:
     * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out].
     *      For output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the following
     *      condition must be satisfied: output_scale > input_scale * filter_scale.
     */
    ANEURALNETWORKS_CONV_2D = 3,

    /** 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]
     *         )
     *
     * Supported tensor types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
     *
     * Supported tensor rank: 4, with "NHWC" data layout.
     *
     * 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.
     * * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
     *      For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32} type, the bias should
     *      also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
     *      For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias
     *      should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and
     *      bias_scale == input_scale * filter_scale.
     * * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
     * * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
     * * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
     * * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
     * * 7: An INT32 value, specifying the stride when walking through input
     *      in the ‘width’ dimension.
     * * 8: An INT32 value, specifying the stride when walking through input
     *      in the ‘height’ dimension.
     * * 9: An INT32 value, specifying the depthwise multiplier.
     * * 10: An INT32 value, and has to be one of the {@link FuseCode} values.
     *       Specifies the activation to invoke on the result of each addition.
     *
     * 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 {@link ANEURALNETWORKS_TENSOR_FLOAT32} type, the bias should
     *      also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
     *      For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias
     *      should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and
     *      bias_scale == input_scale * filter_scale.
     * * 3: An INT32 value, specifying the implicit padding scheme, has to be one of the
     *      {@link PaddingCode} values.
     * * 4: An INT32 value, specifying the stride when walking through input
     *      in the ‘width’ dimension.
     * * 5: An INT32 value, specifying the stride when walking through input
     *      in the ‘height’ dimension.
     * * 6: An INT32 value, specifying the depthwise multiplier.
     * * 7: An INT32 value, and has to be one of the {@link FuseCode} values.
     *       Specifies the activation to invoke on the result of each addition.
     *
     * Outputs:
     * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out].
     *      For output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the following
     *      condition must be satisfied: output_scale > input_scale * filter_scale.
     */
    ANEURALNETWORKS_DEPTHWISE_CONV_2D = 4,

    /** 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 types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
     *
     * Supported tensor rank: 4, with "NHWC" data layout.
     *
     * Inputs:
     * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
     * * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
     *      block_size * block_size must be a divisor of the input depth.
     *
     * Outputs:
     * * 0: The output 4-D tensor, of shape [batch, height*block_size, width*block_size,
     *      depth/(block_size*block_size)].
     */
    ANEURALNETWORKS_DEPTH_TO_SPACE = 5,

    /** Dequantizes the input tensor.
     *
     * The formula is:
     *
     *     output = (input - zeroPoint) * scale.
     *
     * Supported tensor types:
     * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
     *
     * Supported tensor rank: up to 4
     *
     * Inputs:
     * * 0: A tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}.
     *
     * Outputs:
     * * 0: The output tensor of same shape as input0, but with type
     *      {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
     */
    ANEURALNETWORKS_DEQUANTIZE = 6,

    /** 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], we would expect all three values
     * found in Lookups to be  between 0 and 39. The resulting tensor will
     * have shape of [3, 200, 300].
     *
     * If a value in Lookups is out of bounds, the operation will fail
     * and an error will be reported.
     *
     * Inputs:
     * * 0: Lookups. A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32} type.
     *      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.
     */
    ANEURALNETWORKS_EMBEDDING_LOOKUP = 7,

    /** Computes element-wise floor() on the input tensor.
     *
     * Supported tensor types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     *
     * Supported tensor rank: up to 4
     *
     * Inputs:
     * * 0: A tensor.
     *
     * Outputs:
     * * 0: The output tensor, of the same type and dimensions as the input tensor.
     */
    ANEURALNETWORKS_FLOOR = 8,

    /** 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 types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     * * {@link ANEURALNETWORKS_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 ANEURALNETWORKS_TENSOR_FLOAT32} type, the bias should
     *      also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
     *      For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias
     *      should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and
     *      bias_scale == input_scale * filter_scale.
     * * 3: An INT32 value, and has to be one of the {@link FuseCode} values.
     *      Specifies the activation to invoke on the result of each addition.
     *
     * Outputs:
     * * 0: The output tensor, of shape [batch_size, num_units].
     *      For output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the following
     *      condition must be satisfied: output_scale > input_scale * filter_scale.
     */
    ANEURALNETWORKS_FULLY_CONNECTED = 9,

    /** 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 will 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], we're concatenating three slices, so the resulting tensor
     * will have the shape of [3, 200, 300]. If the first entry in
     * Lookups has the value 123456, we'll look for that value in Keys tensor.
     * If the sixth entry of Keys contains 123456, we'll select the sixth
     * slice of Values. If no entry in Keys has 123456, a slice of zeroes
     * will be concatenated.
     *
     * Inputs:
     * * 0: Lookups. A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor with shape [ k ].
     * * 1: Keys. A 1-D {@link ANEURALNETWORKS_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 ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} with offset 0 and scale 1.0f.
     *      A non-zero byte represents True, a hit. A zero indicates otherwise.
     */
    ANEURALNETWORKS_HASHTABLE_LOOKUP = 10,

    /** 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 more dimensions, independently normalizes each 1-D slice along dimension dim.
     *
     * Supported tensor types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     *
     * Supported tensor rank: 4, with "NHWC" data layout (i.e., Num_samples, Height, Width, and Channels).
     *
     * Inputs:
     * * 0: A 4-D tensor, of shape [batches, height, width, depth].
     *
     * Outputs:
     * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
     */
    ANEURALNETWORKS_L2_NORMALIZATION = 11,

    /** 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[batch, row, col, channel] =
     *         sqrt(sum_{i, j} pow(input[batch, row + i, col + j, channel], 2) / sum(1))
     *
     * Supported tensor types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     *
     * Supported tensor rank: 4, with "NHWC" data layout.
     *
     * 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 INT32 value, specifying the padding on the left, in the ‘width’ dimension.
     * * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
     * * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
     * * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
     * * 5: An INT32 value, specifying the stride when walking through input
     *      in the ‘width’ dimension.
     * * 6: An INT32 value, specifying the stride when walking through input
     *      in the ‘height’ dimension.
     * * 7: An INT32 value, specifying the filter width.
     * * 8: An INT32 value, specifying the filter height.
     * * 9: An INT32 value, and has to be one of the {@link FuseCode} values.
     *      Specifies the activation to invoke on the result of each addition.
     *
     * Inputs (implicit padding):
     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
     * * 1: An INT32 value, specifying the implicit padding scheme, has to be one of the
     *      {@link PaddingCode} values.
     * * 2: An INT32 value, specifying the stride when walking through input
     *      in the ‘width’ dimension.
     * * 3: An INT32 value, specifying the stride when walking through input
     *      in the ‘height’ dimension.
     * * 4: An INT32 value, specifying the filter width.
     * * 5: An INT32 value, specifying the filter height.
     * * 6: An INT32 value, and has to be one of the {@link FuseCode} values.
     *      Specifies the activation to invoke on the result of each addition.
     *
     * Outputs:
     * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
     */
    ANEURALNETWORKS_L2_POOL_2D = 12,

    /** 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)
     *
     * Supported tensor types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     *
     * Supported tensor rank: 4, with "NHWC" data layout.
     *
     * Inputs:
     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
     * * 1: An INT32 value, specifying the radius of the normalization window.
     * * 2: A FLOAT32 value, specifying the bias, must not be zero.
     * * 3: A FLOAT32 value, specifying the scale factor, alpha.
     * * 4: A FLOAT32 value, specifying the exponent, beta.
     *
     * Outputs:
     * * 0: The output tensor of same shape as input0.
     */
    ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION = 13,

    /** Computes sigmoid activation on the input tensor element-wise.
     *
     * The output is calculated using this formula:
     *
     *     output = 1 / (1 + exp(-input))
     *
     * Supported tensor types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     * * {@link ANEURALNETWORKS_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 ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type,
     *      the scale must be 1.f / 256 and the zeroPoint must be 0.
     */
    ANEURALNETWORKS_LOGISTIC = 14,

    /**
     * Projects an input to a bit vector via locality senstive hashing.
     *
     * Inputs:
     * * 0: Hash functions. Dim.size == 2, DataType: Float.
     *            Tensor[0].Dim[0]: Number of hash functions.
     *            Tensor[0].Dim[1]: Number of seeds per hash functions.
     *            Tensor[0].Dim[1] <= 32 in sparse case.
     *
     * * 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(=1).
     *          Computed bit vector is considered to be sparse.
     *          Each output element is an int32 made up of multiple bits computed from
     *          hash functions.
     *
     *        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.
     */
    ANEURALNETWORKS_LSH_PROJECTION = 15,

    /**
     * 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.
     *
     * 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.
     *
     * 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"
     *
     * Supported tensor types (type T):
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     *
     * Inputs:
     * * 0: The input (\f$x_t\f$).
     *      A 2-D tensor of type T, 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 type T, 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 type T, of shape [num_units, input_size].
     * * 3: The input-to-cell weights (\f$W_{xc}\f$).
     *      A 2-D tensor of type T, of shape [num_units, input_size].
     * * 4: The input-to-output weights (\f$W_{xo}\f$).
     *      A 2-D tensor of type T, of shape [num_units, input_size].
     * * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional.
     *      A 2-D tensor of type T, 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 type T, of shape [num_units, output_size].
     * * 7: The recurrent-to-cell weights (\f$W_{hc}\f$).
     *      A 2-D tensor of type T, of shape [num_units, output_size].
     * * 8: The recurrent-to-output weights (\f$W_{ho}\f$).
     *      A 2-D tensor of type T, of shape [num_units, output_size].
     * * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional.
     *      A 1-D tensor of type T, of shape [num_units].
     * * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional.
     *      A 1-D tensor of type T, of shape [num_units].
     * * 11:The cell-to-output weights (\f$W_{co}\f$). Optional.
     *      A 1-D tensor of type T, of shape [num_units].
     * * 12:The input gate bias (\f$b_i\f$). Optional.
     *      A 1-D tensor of type T, of shape [num_units].
     * * 13:The forget gate bias (\f$b_f\f$).
     *      A 1-D tensor of type T, of shape [num_units].
     * * 14:The cell bias (\f$b_c\f$).
     *      A 1-D tensor of type T, of shape [num_units].
     * * 15:The output gate bias (\f$b_o\f$).
     *      A 1-D tensor of type T, of shape [num_units].
     * * 16:The projection weights (\f$W_{proj}\f$). Optional.
     *      A 2-D tensor of type T, of shape [output_size, num_units].
     * * 17:The projection bias (\f$b_{proj}\f$). Optional.
     *      A 1-D tensor of type T, of shape [output_size].
     * * 18:The output state (in) (\f$h_{t-1}\f$).
     *      A 2-D tensor of type T, of shape [batch_size, output_size].
     * * 19:The cell state (in) (\f$C_{t-1}\f$).
     *      A 2-D tensor of type T, 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.
     *
     * Outputs:
     * * 0: The scratch buffer.
     *      A 2-D tensor of type T, of shape [batch_size, num_units * 4] with
     *      CIFG, or [batch_size, num_units * 3] without CIFG.
     * * 1: The output state (out) (\f$h_t\f$).
     *      A 2-D tensor of type T, of shape [batch_size, output_size].
     * * 2: The cell state (out) (\f$C_t\f$).
     *      A 2-D tensor of type T, of shape [batch_size, num_units].
     * * 3: The output (\f$o_t\f$).
     *      A 2-D tensor of type T, of shape [batch_size, output_size]. This is
     *      effectively the same as the current “output state (out)” value.
     */
    ANEURALNETWORKS_LSTM = 16,

    /** 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[batch, row, col, channel] =
     *         max_{i, j} (input[batch, row + i, col + j, channel])
     *
     * Supported tensor types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
     *
     * Supported tensor rank: 4, with "NHWC" data layout.
     *
     * 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 INT32 value, specifying the padding on the left, in the ‘width’ dimension.
     * * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
     * * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
     * * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
     * * 5: An INT32 value, specifying the stride when walking through input
     *      in the ‘width’ dimension.
     * * 6: An INT32 value, specifying the stride when walking through input
     *      in the ‘height’ dimension.
     * * 7: An INT32 value, specifying the filter width.
     * * 8: An INT32 value, specifying the filter height.
     * * 9: An INT32 value, and has to be one of the {@link FuseCode} values.
     *      Specifies the activation to invoke on the result of each addition.
     *
     * Inputs (implicit padding):
     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
     * * 1: An INT32 value, specifying the implicit padding scheme, has to be one of the
     *      {@link PaddingCode} values.
     * * 2: An INT32 value, specifying the stride when walking through input
     *      in the ‘width’ dimension.
     * * 3: An INT32 value, specifying the stride when walking through input
     *      in the ‘height’ dimension.
     * * 4: An INT32 value, specifying the filter width.
     * * 5: An INT32 value, specifying the filter height.
     * * 6: An INT32 value, and has to be one of the {@link FuseCode} values.
     *      Specifies the activation to invoke on the result of each addition.
     *
     * Outputs:
     * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
     */
    ANEURALNETWORKS_MAX_POOL_2D = 17,

    /** Multiplies two tensors, element-wise.
     *
     * Takes two input tensors of identical type 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 types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
     *
     * Supported tensor rank: up to 4
     *
     * Inputs:
     * * 0: A tensor.
     * * 1: A tensor of the same type, and compatible dimensions as input0.
     * * 2: An INT32 value, and has to be one of the {@link FuseCode} values.
     *      Specifies the activation to invoke on the result of each addition.
     *
     * Outputs:
     * * 0: The product, a tensor of the same type as input0.
     *      For output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the following
     *      condition must be satisfied: output_scale > input1_scale * input2_scale.
     */
    ANEURALNETWORKS_MUL = 18,

    /** Computes rectified linear activation on the input tensor element-wise.
     *
     * The output is calculated using this formula:
     *
     *     output = max(0, input)
     *
     * Supported tensor types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     * * {@link ANEURALNETWORKS_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.
     */
    ANEURALNETWORKS_RELU = 19,

    /** 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 types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     * * {@link ANEURALNETWORKS_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.
     */
    ANEURALNETWORKS_RELU1 = 20,

    /** 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 types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     * * {@link ANEURALNETWORKS_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.
     */
    ANEURALNETWORKS_RELU6 = 21,

    /** 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 types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     * * {@link ANEURALNETWORKS_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 type {@link ANEURALNETWORKS_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.
     */
    ANEURALNETWORKS_RESHAPE = 22,

    /** Resizes images to given size using the bilinear interpretation.
     *
     * Resized images will 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 types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     *
     * Supported tensor rank: 4, with "NHWC" data layout.
     *
     * Inputs:
     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
     * * 1: An INT32 value, specifying the output height of the output tensor.
     * * 2: An INT32 value, specifying the output width of the output tensor.
     *
     * Outputs:
     * * 0: The output 4-D tensor, of shape [batches, new_height, new_width, depth].
     */
    ANEURALNETWORKS_RESIZE_BILINEAR = 23,

    /**
     * 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 types (Type T):
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     *
     * Inputs:
     * * 0: input.
     *      A 2-D tensor of type T, 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 type T, of shape [num_units, input_size], where
     *      “num_units” corresponds to the number of units.
     * * 2: recurrent_weights.
     *      A 2-D tensor of type T, of shape [num_units, num_units], with columns
     *      corresponding to the weights from each unit.
     * * 3: bias.
     *      A 1-D tensor of type T, of shape [num_units].
     * * 4: hidden state (in).
     *      A 2-D tensor of type T, of shape [batch_size, num_units].
     * * 5: fused_activation_function.
     *      An optional {@link FuseCode} 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 type T, of shape [batch_size, num_units].
     *
     * * 1: output.
     *      A 2-D tensor of type T, of shape [batch_size, num_units]. This is
     *      effectively the same as the current state value.
     */
    ANEURALNETWORKS_RNN = 24,

    /** 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)}
     *
     * Supported tensor types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
     *
     * Supported tensor rank: 2 or 4.
     *
     * Inputs:
     * * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
     * * 1: A FLOAT32 value, specifying the positive scaling factor for the exponent, beta.
     *
     * Outputs:
     * * 0: The output tensor of same shape as input0.
     *      For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type,
     *      the scale must be 1.f / 256 and the zeroPoint must be 0.
     */
    ANEURALNETWORKS_SOFTMAX = 25,

    /** 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 types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
     *
     * Supported tensor rank: 4, with "NHWC" data layout.
     *
     * Inputs:
     * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
     * * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
     *      block_size must be a divisor of both the input height and width.
     *
     * Outputs:
     * * 0: The output 4-D tensor, of shape [batch, height/block_size, width/block_size,
     *      depth*block_size*block_size].
     */
    ANEURALNETWORKS_SPACE_TO_DEPTH = 26,

    /**
     * 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, "ANEURALNETWORKS_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 types (type T):
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     *
     * Inputs:
     * * 0: input.
     *      A 2-D tensor of type T, 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 type T, of shape [num_units, input_size], where
     *      “num_units” corresponds to the number of units.
     * * 2: weights_time.
     *      A 2-D tensor of type T, 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 type T, of shape [num_units].
     * * 4: state (in).
     *      A 2-D tensor of type T, 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 FuseCode} 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 type T, of shape [batch_size, (memory_size - 1) * num_units * rank].
     * * 1: output.
     *      A 2-D tensor of type T, of shape [batch_size, num_units].
     */
    ANEURALNETWORKS_SVDF = 27,

    /** Computes hyperbolic tangent of input tensor element-wise.
     *
     * The output is calculated using this formula:
     *
     *     output = tanh(input)
     *
     * Supported tensor types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     *
     * Supported tensor rank: up to 4.
     *
     * Inputs:
     * * 0: A tensor, specifying the input.
     *
     * Outputs:
     * * 0: The output tensor of same shape as input0.
     */
    ANEURALNETWORKS_TANH = 28,

// TODO: change to __ANDROID_API__ >= __ANDROID_API_P__ once available.
#if __ANDROID_API__ > __ANDROID_API_O_MR1__
    // TODO: make the description easier to understand.
    /**
     * 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 types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
     *
     * Supported tensor rank: 4
     *
     * Inputs:
     * 0: An n-D tensor, specifying the tensor to be reshaped
     * 1: A 1-D Tensor of type TENSOR_INT32, the block sizes for each spatial dimension of the
     *    input tensor. All values must be >= 1.
     *
     * Outputs:
     * 0: A tensor of the same type as input0.
     */
    ANEURALNETWORKS_BATCH_TO_SPACE_ND = 29,

    /**
     * Element-wise division of two tensors.
     *
     * Takes two input tensors of identical type 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 types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     *
     * Supported tensor rank: up to 4
     *
     * Inputs:
     * 0: An n-D tensor, specifying the first input.
     * 1: A tensor of the same type, and compatible dimensions as input0.
     * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
     *    Specifies the activation to invoke on the result of each addition.
     *
     * Outputs:
     * 0: A tensor of the same type as input0.
     */
    ANEURALNETWORKS_DIV = 30,

    /**
     * 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 types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
     *
     * Supported tensor rank: up to 4
     *
     * Inputs:
     * 0: A tensor, specifying the input.
     * 1: A 1-D Tensor of type 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 INT32 value, keep_dims. If positive, retains reduced dimensions with length 1.
     *
     * Outputs:
     * 0: A tensor of the same type as input0.
     */
    ANEURALNETWORKS_MEAN = 31,

    /**
     * Pads a tensor.
     *
     * This operation pads a tensor according to the specified paddings.
     *
     * Supported tensor types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     * * {@link ANEURALNETWORKS_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 type 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 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.
     *
     * Outputs:
     * 0: A tensor of the same type as input0.
     */
    ANEURALNETWORKS_PAD = 32,

    // TODO: make the description easier to understand.
    /**
     * 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 types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
     *
     * Supported tensor rank: 4
     *
     * Inputs:
     * 0: An n-D tensor, specifying the input.
     * 1: A 1-D Tensor of type TENSOR_INT32, the block sizes for each spatial dimension of the
     *    input tensor. All values must be >= 1.
     * 2: A 2-D Tensor of type TENSOR_INT32, the paddings for each spatial diemension 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.
     *
     * Outputs:
     * 0: A tensor of the same type as input0.
     */
    ANEURALNETWORKS_SPACE_TO_BATCH_ND = 33,

    /**
     * Removes dimensions of size 1 from the shape of a tensor.
     *
     * Given a tensor input, this operation returns a tensor of the same type 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 types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     * * {@link ANEURALNETWORKS_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 type 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 will be reported if squeezing a dimension that
     *    is not 1.
     *
     * Outputs:
     * 0: A tensor of the same type as input0. Contains the same data as input, but has one or more
     *    dimensions of size 1 removed.
     */
    ANEURALNETWORKS_SQUEEZE = 34,

    /**
     * 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 types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
     *
     * Supported tensor rank: up to 4
     *
     * Inputs:
     * 0: An n-D tensor, specifying the tensor to be sliced.
     * 1: A 1-D Tensor of type TENSOR_INT32, the starts of the dimensions of the input
     *    tensor to be sliced. The length must be of rank(input0).
     * 2: A 1-D Tensor of type TENSOR_INT32, the ends of the dimensions of the input
     *    tensor to be sliced. The length must be of rank(input0).
     * 3: A 1-D Tensor of type TENSOR_INT32, the strides of the dimensions of the input
     *    tensor to be sliced. The length must be of rank(input0).
     * 4: An INT32 value, begin_mask. 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: An INT32 value, end_mask. 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: An INT32 value, shrink_axis_mask. An int32 mask. If the ith bit of shrink_axis_mask is
     *    set, it implies that the ith specification shrinks the dimensionality by 1. A slice of
     *    size 1 starting from begin[i] in the dimension will be preserved.
     *
     * Outputs:
     * 0: A tensor of the same type as input0.
     */
    ANEURALNETWORKS_STRIDED_SLICE = 35,

    /**
     * Element-wise subtraction of two tensors.
     *
     * Takes two input tensors of identical type 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 types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     *
     * Supported tensor rank: up to 4
     *
     * Inputs:
     * 0: An n-D tensor, specifying the first input.
     * 1: A tensor of the same type, and compatible dimensions as input0.
     * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
     *    Specifies the activation to invoke on the result of each addition.
     *
     * Outputs:
     * 0: A tensor of the same type as input0.
     */
    ANEURALNETWORKS_SUB = 36,

    /**
     * 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 types:
     * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
     * * {@link ANEURALNETWORKS_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 type TENSOR_INT32, the permutation of the dimensions of the
     *    input tensor.
     *
     * Outputs:
     * 0: A tensor of the same type as input0.
     */
    ANEURALNETWORKS_TRANSPOSE = 37,
#endif
} OperationCode;

/**
 * Fused activation function types.
 *
 */
typedef enum {
    /** NO fused activation function. */
    ANEURALNETWORKS_FUSED_NONE = 0,
    /** Fused ReLU activation function. */
    ANEURALNETWORKS_FUSED_RELU = 1,
    /** Fused ReLU1 activation function. */
    ANEURALNETWORKS_FUSED_RELU1 = 2,
    /** Fused ReLU6 activation function. */
    ANEURALNETWORKS_FUSED_RELU6 = 3,
} FuseCode;

/**
 * Implicit padding algorithms.
 *
 */
typedef enum {
    /**
     * SAME padding.
     * Padding on both ends are the "same":
     *     padding_to_beginning =  total_padding / 2
     *     padding_to_end       = (total_padding + 1)/2.
     * i.e., for even number of padding, padding to both ends are exactly
     * the same; for odd number of padding, padding to the ending is bigger
     * than the padding to the beginning by 1.
     *
     * total_padding is a function of input, stride and filter size.
     * It could be computed as follows:
     *    out_size = (input + stride - 1) / stride;
     *    needed_input = (out_size - 1) * stride + filter_size
     *    total_padding = max(0, needed_input - output_size)
     *  The computation is the same for the horizontal and vertical directions.
     */
    ANEURALNETWORKS_PADDING_SAME = 1,

    /**
     * VALID padding.
     * No padding. When the input size is not evenly divisible by
     * the filter size, the input at the end that could not fill
     * the whole filter tile will simply be ignored.
     */
    ANEURALNETWORKS_PADDING_VALID = 2,
} PaddingCode;

/**
 * Execution preferences.
 */
typedef enum {
    /**
     * Prefer executing in a way that minimizes battery drain.
     * This is desirable for compilations that will be executed often.
     */
    ANEURALNETWORKS_PREFER_LOW_POWER = 0,
    /**
     * Prefer returning a single answer as fast as possible, even if this causes
     * more power consumption.
     */
    ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWER = 1,
    /**
     * Prefer maximizing the throughput of successive frames, for example when
     * processing successive frames coming from the camera.
     */
    ANEURALNETWORKS_PREFER_SUSTAINED_SPEED = 2,
} PreferenceCode;

/**
 * Result codes.
 */
typedef enum {
    ANEURALNETWORKS_NO_ERROR = 0,
    ANEURALNETWORKS_OUT_OF_MEMORY = 1,
    ANEURALNETWORKS_INCOMPLETE = 2,
    ANEURALNETWORKS_UNEXPECTED_NULL = 3,
    ANEURALNETWORKS_BAD_DATA = 4,
    ANEURALNETWORKS_OP_FAILED = 5,
    ANEURALNETWORKS_BAD_STATE = 6,
    ANEURALNETWORKS_UNMAPPABLE = 7,
} ResultCode;

/**
 * For {@link ANeuralNetworksModel_setOperandValue}, values with a
 * length smaller or equal to this will be immediately copied into
 * the model. The size is in bytes.
 */
enum {
    ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES = 128
};

/**
 * ANeuralNetworksMemory is an opaque type that represents memory.
 *
 * This type is used to represent shared memory, memory mapped files,
 * and similar memories.
 *
 * By using shared memory, a program can efficiently communicate to the
 * runtime and drivers the tensors that define a model. See
 * {@link ANeuralNetworksModel_setOperandValueFromMemory}. An application
 * should typically create one shared memory object that contains every tensor
 * needed to define a model. {@link ANeuralNetworksMemory_createFromFd} can be
 * used to create shared memory from a file handle.
 *
 * Memory objects can also be used to specify the input and output arguments of
 * an execution. See {@link ANeuralNetworksExecution_setInputFromMemory}
 * and {@link ANeuralNetworksExecution_setOutputFromMemory}.
 */
typedef struct ANeuralNetworksMemory ANeuralNetworksMemory;

/**
 * ANeuralNetworksModel is an opaque type that contains a description of the
 * mathematical operations that constitute the model.
 *
 * <p>Build the model by calling<ul>
 * <li>{@link ANeuralNetworksModel_create}</li>
 * <li>{@link ANeuralNetworksModel_addOperation}</li>
 * <li>{@link ANeuralNetworksModel_addOperand}</li>
 * </ul>
 *
 * A model is completed by calling {@link ANeuralNetworksModel_finish}.
 * A model is destroyed by calling {@link ANeuralNetworksModel_free}.
 *
 * <p>A model cannot be modified once {@link ANeuralNetworksModel_finish}
 * has been called on it.</p>
 *
 * <p>It is the application's responsibility to make sure that only one thread
 * modifies a model at a given time. It is however safe for more than one
 * thread to use the model once {@link ANeuralNetworksModel_finish} has returned.</p>
 *
 * <p>It is also the application's responsibility to ensure that there are no other
 * uses of the model after calling {@link ANeuralNetworksModel_free}.
 * This includes any compilation or execution object created using the model.</p>
 */
typedef struct ANeuralNetworksModel ANeuralNetworksModel;

/**
 * ANeuralNetworksCompilation is an opaque type that can be used to compile
 * a machine learning model.
 *
 * <p>To use:<ul>
 *    <li>Create a new compilation instance by calling the
 *        {@link ANeuralNetworksCompilation_create} function.</li>
 *    <li>Set any desired properties on the compilation (for example,
 *        {@link ANeuralNetworksCompilation_setPreference}).</li>
 *    <li>Complete the compilation with {@link ANeuralNetworksCompilation_finish}.</li>
 *    <li>Use the compilation as many times as needed
 *        with {@link ANeuralNetworksExecution_create}.</li>
 *    <li>Destroy the compilation with {@link ANeuralNetworksCompilation_free}
 *        once all executions using the compilation have completed.</li></ul></p>
 *
 * A compilation is completed by calling {@link ANeuralNetworksCompilation_finish}.
 * A compilation is destroyed by calling {@link ANeuralNetworksCompilation_free}.
 *
 * <p>A compilation cannot be modified once {@link ANeuralNetworksCompilation_finish}
 * has been called on it.</p>
 *
 * <p>It is the application's responsibility to make sure that only
 * one thread modifies a compilation at a given time. It is however
 * safe for more than one thread to use the compilation once
 * {@link ANeuralNetworksCompilation_finish} has returned.</p>
 *
 * <p>It is also the application's responsibility to ensure that there are no other
 * uses of the compilation after calling {@link ANeuralNetworksCompilation_free}.
 * This includes any execution object created using the compilation.</p>
 */
typedef struct ANeuralNetworksCompilation ANeuralNetworksCompilation;

/**
 * ANeuralNetworksExecution is an opaque type that can be used to apply a machine
 * learning model to a set of inputs.
 *
 * <p>To use:<ul>
 *    <li>Create a new execution instance by calling the
 *        {@link ANeuralNetworksExecution_create} function.</li>
 *    <li>Associate data to the model inputs with
 *        {@link ANeuralNetworksExecution_setInput} or
 *        {@link ANeuralNetworksExecution_setInputFromMemory}.</li>
 *    <li>Associate output buffers to the model outputs with
 *        {@link ANeuralNetworksExecution_setOutput} or
 *        {@link ANeuralNetworksExecution_setOutputFromMemory}.</li>
 *    <li>Apply the model with {@link ANeuralNetworksExecution_startCompute}.</li>
 *    <li>Wait for the execution to complete with {@link
 *        ANeuralNetworksEvent_wait}.</li>
 *    <li>Destroy the execution with
 *        {@link ANeuralNetworksExecution_free}.</li></ul></p>
 *
 * <p>An execution cannot be modified once {@link ANeuralNetworksExecution_startCompute}
 * has been called on it.</p>
 *
 * <p>An execution can be applied to a model with
 * {@link ANeuralNetworksExecution_startCompute} only once. Create new executions
 * to do new evaluations of the model.</p>
 *
 * <p>It is the application's responsibility to make sure that only one thread
 * modifies an execution at a given time. It is however safe for more than one
 * thread to use {@link ANeuralNetworksEvent_wait} at the same time.</p>
 *
 * <p>It is also the application's responsibility to ensure that there are no other
 * uses of the request after calling {@link ANeuralNetworksExecution_free}.</p>
 */
typedef struct ANeuralNetworksExecution ANeuralNetworksExecution;

/**
 * ANeuralNetworksOperandType describes the type of an operand.
 * This structure is used to describe both scalars and tensors.
 */
typedef struct ANeuralNetworksOperandType {
    /** The data type, e.g ANEURALNETWORKS_INT8. */
    int32_t type;
    /** The number of dimensions. It should be 0 for scalars. */
    uint32_t dimensionCount;
    /** The dimensions of the tensor. It should be nullptr for scalars. */
    const uint32_t* dimensions;
    /** These two fields are only used for quantized tensors.
     * They should be zero for scalars and non-fixed point tensors.
     * The dequantized value of each entry is (value - zeroPoint) * scale.
     */
    float scale;
    int32_t zeroPoint;
} ANeuralNetworksOperandType;

typedef int32_t ANeuralNetworksOperationType;

/**
 * ANeuralNetworksEvent is an opaque type that represents an event
 * that will be signaled once an execution completes.
 */
typedef struct ANeuralNetworksEvent ANeuralNetworksEvent;


/**
 * Creates a shared memory object from a file descriptor.
 *
 * The shared memory is backed by a file descriptor via mmap.
 * See {@link ANeuralNetworksMemory} for a description on how to use
 * this shared memory.
 *
 * @param size The requested size in bytes.
 *             Must not be larger than the file size.
 * @param prot The desired memory protection for the mapping.
 *             It is either PROT_NONE or the bitwise OR of one or
 *             more of the following flags: PROT_READ, PROT_WRITE.
 * @param fd The requested file descriptor.
 *           The file descriptor has to be mmap-able. The file
 *           descriptor will be duplicated.
 * @param offset The offset to the beginning of the file of the area to map.
 *               The offset has to be aligned to a page size.
 * @param memory The memory object to be created.
 *               Set to NULL if unsuccessful.
 *
 * @return ANEURALNETWORKS_NO_ERROR if the request completed normally.
 */
int ANeuralNetworksMemory_createFromFd(size_t size, int protect, int fd, size_t offset,
                                       ANeuralNetworksMemory** memory);

/**
 * Delete a memory object.
 *
 * Destroys the object used by the run time to keep track of the memory.
 * This will free the underlying actual memory if no other code has open
 * handles to this memory.
 *
 * @param memory The memory object to be freed.
 */
void ANeuralNetworksMemory_free(ANeuralNetworksMemory* memory);

/**
 * Create an empty {@link ANeuralNetworksModel}.
 *
 * <p>This only creates the object. Computation is performed once
 * {@link ANeuralNetworksExecution_startCompute} is invoked.
 *
 * The model should be constructed with calls to
 * {@link ANeuralNetworksModel_addOperation} and
 * {@link ANeuralNetworksModel_addOperand}
 *
 * <p>{@link ANeuralNetworksModel_finish} should be called once the model
 * has been fully constructed.</p>
 *
 * <p>{@link ANeuralNetworksModel_free} should be called once the model
 * is no longer needed.</p>
 *
 * @param model The {@link ANeuralNetworksModel} to be created.
 *              Set to NULL if unsuccessful.
 *
 * @return ANEURALNETWORKS_NO_ERROR if successful.
 */
int ANeuralNetworksModel_create(ANeuralNetworksModel** model);

/**
 * Destroy a model.
 *
 * The model need not have been finished by a call to
 * {@link ANeuralNetworksModel_finish}.
 *
 * See {@link ANeuralNetworksModel} for information on multithreaded usage.
 *
 * @param model The model to be destroyed. Passing NULL is acceptable and
 *              results in no operation.
 */
void ANeuralNetworksModel_free(ANeuralNetworksModel* model);

/**
 * Indicate that we have finished modifying a model. Required before
 * calling {@link ANeuralNetworksCompilation_create}.
 *
 * An application is responsible to make sure that no other thread uses
 * the model at the same time.
 *
 * This function must only be called once for a given model.
 *
 * See {@link ANeuralNetworksModel} for information on multithreaded usage.
 *
 * @param model The model to be finished.
 *
 * @return ANEURALNETWORKS_NO_ERROR if successful.
 */
int ANeuralNetworksModel_finish(ANeuralNetworksModel* model);

/**
 * Add an operand to a model.
 *
 * The order in which the operands are added is important. The first one added
 * to a model will have the index value 0, the second 1, etc. These indexes are
 * used as operand identifiers in {@link ANeuralNetworksModel_addOperation},
 * {@link ANeuralNetworksExecution_setInput},
 * {@link ANeuralNetworksExecution_setInputFromMemory},
 * {@link ANeuralNetworksExecution_setOutput},
 * {@link ANeuralNetworksExecution_setOutputFromMemory} and
 * {@link ANeuralNetworksExecution_setOperandValue}.
 *
 * To build a model that can accomodate inputs of various sizes, as you may want
 * to do for a CNN, set the size of the dimensions that will vary at run time to 0.
 * If you do so, provide the full dimensions when calling
 * {@link ANeuralNetworksExecution_setInput} or {@link ANeuralNetworksExecution_setInputFromMemory}.
 *
 * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been
 * called will return an error.
 *
 * See {@link ANeuralNetworksModel} for information on multithreaded usage.
 *
 * @param model The model to be modified.
 * @param type The {@link ANeuralNetworksOperandType} that describes the shape
 * of the operand.
 *
 * @return ANEURALNETWORKS_NO_ERROR if successful.
 */
int ANeuralNetworksModel_addOperand(ANeuralNetworksModel* model,
                                    const ANeuralNetworksOperandType* type);

/**
 * Sets an operand to a constant value.
 *
 * Values of length smaller or equal to
 * {@link ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES}
 * are immediately copied into the model.
 *
 * For values of length greater than {@link ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES},
 * a pointer to the buffer is stored within the model. The application is responsible
 * for not changing the content of this region until all executions using this model
 * have completed. As the data may be copied during processing, modifying the data
 * after this call yields undefined results.
 *
 * For large tensors, using {@link ANeuralNetworksModel_setOperandValueFromMemory}
 * is likely to be more efficient.
 *
 * To indicate that an optional operand should be considered missing,
 * pass nullptr for buffer and 0 for length.
 *
 * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been
 * called will return an error.
 *
 * See {@link ANeuralNetworksModel} for information on multithreaded usage.
 *
 * @param model The model to be modified.
 * @param index The index of the model operand we're setting.
 * @param buffer A pointer to the data to use.
 * @param length The size in bytes of the data value.
 *
 * @return ANEURALNETWORKS_NO_ERROR if successful.
 */
int ANeuralNetworksModel_setOperandValue(ANeuralNetworksModel* model, int32_t index,
                                         const void* buffer, size_t length);

/**
 * Sets an operand to a value stored in a memory object.
 *
 * The content of the memory is not copied. A reference to that memory is stored
 * inside the model. The application is responsible for not changing the content
 * of the memory region until all executions using this model have completed.
 * As the data may be copied during processing, modifying the data after this call
 * yields undefined results.
 *
 * To indicate that an optional operand should be considered missing,
 * use {@link ANeuralNetworksModel_setOperandValue} instead, passing nullptr for buffer.
 *
 * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been
 * called will return an error.
 *
 * See {@link ANeuralNetworksModel} for information on multithreaded usage.
 *
 * @param model The model to be modified.
 * @param index The index of the model operand we're setting.
 * @param buffer A pointer to the data to use.
 * @param memory The memory containing the data.
 * @param offset This specifies the location of the data within the memory.
 *               The offset is in bytes from the start of memory.
 * @param length The size in bytes of the data value.
 *
 * @return ANEURALNETWORKS_NO_ERROR if successful.
 */
int ANeuralNetworksModel_setOperandValueFromMemory(ANeuralNetworksModel* model, int32_t index,
                                                   const ANeuralNetworksMemory* memory,
                                                   size_t offset, size_t length);

/**
 * Add an operation to a model.
 *
 * @param model The model to be modified.
 * @param type The type of the operation.
 * @param inputCount The number of entries in the inputs array.
 * @param inputs An array of indexes identifying each operand.
 * @param outputCount The number of entries in the outputs array.
 * @param outputs An array of indexes identifying each operand.
 *
 * The operands specified by inputs and outputs must have been
 * previously added by calls to {@link ANeuralNetworksModel_addOperand}.
 *
 * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been
 * called will return an error.
 *
 * See {@link ANeuralNetworksModel} for information on multithreaded usage.
 *
 * @return ANEURALNETWORKS_NO_ERROR if successful.
 */
int ANeuralNetworksModel_addOperation(ANeuralNetworksModel* model,
                                      ANeuralNetworksOperationType type, uint32_t inputCount,
                                      const uint32_t* inputs, uint32_t outputCount,
                                      const uint32_t* outputs);

/**
 * Specifies which operands will be the model's inputs and outputs.
 *
 * An operand cannot be used for both input and output. Doing so will
 * return an error.
 *
 * @param model The model to be modified.
 * @param inputCount The number of entries in the inputs array.
 * @param inputs An array of indexes identifying the input operands.
 * @param outputCount The number of entries in the outputs array.
 * @param outputs An array of indexes identifying the output operands.
 *
 * The operands specified by inputs and outputs must have been
 * previously added by calls to {@link ANeuralNetworksModel_addOperand}.
 *
 * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been
 * called will return an error.
 *
 * See {@link ANeuralNetworksModel} for information on multithreaded usage.
 *
 */
int ANeuralNetworksModel_identifyInputsAndOutputs(ANeuralNetworksModel* model, uint32_t inputCount,
                                                  const uint32_t* inputs, uint32_t outputCount,
                                                  const uint32_t* outputs);

/**
 * Specifies whether {@link ANEURALNETWORKS_TENSOR_FLOAT32} is allowed to be
 * calculated with range and/or precision as low as that of the IEEE 754 16-bit
 * floating-point format. By default, {@link ANEURALNETWORKS_TENSOR_FLOAT32}
 * must be calculated using at least the range and precision of the IEEE 754
 * 32-bit floating-point format.
 *
 * @param model The model to be modified.
 * @param allow 'true' indicates {@link ANEURALNETWORKS_TENSOR_FLOAT32} may be
 *              calculated with range and/or precision as low as that of the
 *              IEEE 754 16-bit floating point format. 'false' indicates
 *              {@link ANEURALNETWORKS_TENSOR_FLOAT32} must be calculated using
 *              at least the range and precision of the IEEE 754 32-bit floating
 *              point format.
 *
 * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been
 * called will return an error.
 *
 * See {@link ANeuralNetworksModel} for information on multithreaded usage.
 */
int ANeuralNetworksModel_relaxComputationFloat32toFloat16(ANeuralNetworksModel* model, bool allow);

/**
 * Create a {@link ANeuralNetworksCompilation} to compile the given model.
 *
 * <p>This only creates the object. Compilation is only performed once
 * {@link ANeuralNetworksCompilation_finish} is invoked.</p>
 *
 * <p>{@link ANeuralNetworksCompilation_finish} should be called once
 * all desired properties have been set on the compilation.</p>
 *
 * <p>{@link ANeuralNetworksModel_free} should be called once the compilation
 * is no longer needed.</p>
 *
 * <p>The provided model must outlive the compilation.</p>
 *
 * The model must already have been finished by a call to
 * {@link ANeuralNetworksModel_finish}.
 *
 * See {@link ANeuralNetworksCompilation} for information on multithreaded usage.
 *
 * @param model The {@link ANeuralNetworksModel} to be compiled.
 * @param compilation The newly created object or NULL if unsuccessful.
 *
 * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA
 *         if the model is invalid.
 */
int ANeuralNetworksCompilation_create(ANeuralNetworksModel* model,
                                      ANeuralNetworksCompilation** compilation);

/**
 * Destroy a compilation.
 *
 * The compilation need not have been finished by a call to
 * {@link ANeuralNetworksModel_finish}.
 *
 * See {@link ANeuralNetworksCompilation} for information on multithreaded usage.
 *
 * @param compilation The compilation to be destroyed. Passing NULL is acceptable and
 *                    results in no operation.
 */
void ANeuralNetworksCompilation_free(ANeuralNetworksCompilation* compilation);

/**
 * Sets the execution preference.
 *
 * <p>Provides guidance to the runtime when trade-offs are possible.</p>
 *
 * See {@link ANeuralNetworksCompilation} for information on multithreaded usage.
 *
 * @param compilation The compilation to be modified.
 * @param preference Either {@link PREFER_LOW_POWER},
 *                  {@link PREFER_SINGLE_FAST_ANSWER}, or
 *                  {@link PREFER_SUSTAINED_SPEED}.
 *
 * @return ANEURALNETWORKS_NO_ERROR if successful.
 */
int ANeuralNetworksCompilation_setPreference(ANeuralNetworksCompilation* compilation,
                                             int32_t preference);

/**
 * Indicate that we have finished modifying a compilation. Required before
 * calling {@link ANeuralNetworksExecution_create}.
 *
 * An application is responsible to make sure that no other thread uses
 * the compilation at the same time.
 *
 * This function must only be called once for a given compilation.
 *
 * See {@link ANeuralNetworksCompilation} for information on multithreaded usage.
 *
 * @param compilation The compilation to be finished.
 *
 * @return ANEURALNETWORKS_NO_ERROR if successful.
 */
int ANeuralNetworksCompilation_finish(ANeuralNetworksCompilation* compilation);

/**
 * Create a {@link ANeuralNetworksExecution} to apply the given compilation.
 * This only creates the object. Computation is only performed once
 * {@link ANeuralNetworksExecution_startCompute} is invoked.
 *
 * <p>The provided compilation must outlive the execution.</p>
 *
 * See {@link ANeuralNetworksExecution} for information on multithreaded usage.
 *
 * @param compilation The {@link ANeuralNetworksCompilation} to be evaluated.
 * @param execution The newly created object or NULL if unsuccessful.
 *
 * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA
 *         if the compilation is invalid.
 */
int ANeuralNetworksExecution_create(ANeuralNetworksCompilation* compilation,
                                    ANeuralNetworksExecution** execution);

/**
 * Destroy an execution.
 *
 * <p>If called on an execution for which
 * {@link ANeuralNetworksExecution_startCompute} has been called, the
 * function will return immediately but will mark the execution to be deleted
 * once the computation completes. The related {@link ANeuralNetworksEvent}
 * will be signaled and the {@link ANeuralNetworksEvent_wait} will return
 * ANEURALNETWORKS_ERROR_DELETED.
 *
 * See {@link ANeuralNetworksExecution} for information on multithreaded usage.
 *
 * @param execution The execution to be destroyed. Passing NULL is acceptable and
 *                  results in no operation.
 */
void ANeuralNetworksExecution_free(ANeuralNetworksExecution* execution);

/**
 * Associate a user buffer with an input of the model of the
 * {@link ANeuralNetworksExecution}.
 *
 * <p>The provided buffer must outlive the execution.</p>
 *
 * If the input is optional, you can indicate that it is omitted by
 * passing nullptr for buffer and 0 for length.
 *
 * See {@link ANeuralNetworksExecution} for information on multithreaded usage.
 *
 * @param execution The execution to be modified.
 * @param index The index of the input argument we are setting. It is
 *              an index into the lists passed to
 *              {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not
 *              the index associated with {@link ANeuralNetworksModel_addOperand}.
 * @param type The type of the operand. This should be used to specify the
 *             dimensions that were set to 0 when the operand was added to the
 *             model. All other properties of the type must be the same as
 *             specified in the model. If the type is the same as specified
 *             when the model was built, NULL can be passed.
 * @param buffer The buffer containing the data.
 * @param length The length in bytes of the buffer.
 *
 * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the
 *         name is not recognized or the buffer is too small for the input.
 */
int ANeuralNetworksExecution_setInput(ANeuralNetworksExecution* execution, int32_t index,
                                      const ANeuralNetworksOperandType* type, const void* buffer,
                                      size_t length);

/**
 * Associate part of a memory object with an input of the model of the
 * {@link ANeuralNetworksExecution}.
 *
 * <p>The provided memory must outlive the execution.</p>
 *
 * If the input is optional, you can indicate that it is omitted by
 * using {@link ANeuralNetworks_setInput} instead, passing nullptr for buffer
 * and 0 for length.
 *
 * See {@link ANeuralNetworksExecution} for information on multithreaded usage.
 *
 * @param execution The execution to be modified.
 * @param index The index of the input argument we are setting. It is
 *              an index into the lists passed to
 *              {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not
 *              the index associated with {@link ANeuralNetworksModel_addOperand}.
 * @param type The type of the operand. This can be used to specify the
 *             dimensions that were set to 0 when the operand was added to the
 *             model. All other values must be the same as specified in the
 *             model. If the type is the same as specified when the model
 *             was built, NULL can be passed.
 * @param memory The memory containing the data.
 * @param offset This specifies the location of the data within the memory.
 *               The offset is in bytes from the start of memory.
 * @param length The size in bytes of the data value.
 *
 * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the
 *         name is not recognized or the buffer is too small for the input.
 */
int ANeuralNetworksExecution_setInputFromMemory(ANeuralNetworksExecution* execution, int32_t index,
                                                const ANeuralNetworksOperandType* type,
                                                const ANeuralNetworksMemory* memory, size_t offset,
                                                size_t length);

/**
 * Associate a user buffer with an output of the model of the
 * {@link ANeuralNetworksExecution}.
 *
 * If the output is optional, you can indicate that it is omitted by
 * passing nullptr for buffer and 0 for length.
 *
 * <p>The provided buffer must outlive the execution.</p>
 *
 * See {@link ANeuralNetworksExecution} for information on multithreaded usage.
 *
 * @param execution The execution to be modified.
 * @param index The index of the output argument we are setting. It is
 *              an index into the lists passed to
 *              {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not
 *              the index associated with {@link ANeuralNetworksModel_addOperand}.
 * @param type The type of the operand. This can be used to specify the
 *             dimensions that were set to 0 when the operand was added to the
 *             model. All other values must be the same as specified in the
 *             model. If the type is the same as specified when the model
 *             was built, NULL can be passed.
 * @param buffer The buffer where the data is to be written.
 * @param length The length in bytes of the buffer.
 *
 * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the
 *         name is not recognized or the buffer is too small for the output.
 */
int ANeuralNetworksExecution_setOutput(ANeuralNetworksExecution* execution, int32_t index,
                                       const ANeuralNetworksOperandType* type, void* buffer,
                                       size_t length);

/**
 * Associate part of a memory object with an output of the model of the
 * {@link ANeuralNetworksExecution}.
 *
 * If the output is optional, you can indicate that it is omitted by
 * using {@link ANeuralNetworks_setOutput} instead, passing nullptr for buffer
 * and 0 for length.
 *
 * <p>The provided memory must outlive the execution.</p>
 *
 * See {@link ANeuralNetworksExecution} for information on multithreaded usage.
 *
 * @param execution The execution to be modified.
 * @param index The index of the output argument we are setting. It is
 *              an index into the lists passed to
 *              {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not
 *              the index associated with {@link ANeuralNetworksModel_addOperand}.
 * @param type The type of the operand. This can be used to specify the
 *             dimensions that were set to 0 when the operand was added to the
 *             model. All other values must be the same as specified in the
 *             model. If the type is the same as specified when the model
 *             was built, NULL can be passed.
 * @param memory The memory where the data is to be stored.
 * @param offset This specifies the location of the data within the memory.
 *               The offset is in bytes from the start of memory.
 * @param length The length in bytes of the data value.
 *
 * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the
 *         name is not recognized or the buffer is too small for the output.
 */
int ANeuralNetworksExecution_setOutputFromMemory(ANeuralNetworksExecution* execution, int32_t index,
                                                 const ANeuralNetworksOperandType* type,
                                                 const ANeuralNetworksMemory* memory, size_t offset,
                                                 size_t length);

/**
 * Schedule evaluation of the execution.
 *
 * <p>Schedules evaluation of the execution. Once the model has been
 * applied and the outputs are ready to be consumed, the returned event will be
 * signaled. Use {@link ANeuralNetworksEvent_wait} to wait for that event.
 * </p>
 *
 * Multiple executions can be scheduled and evaluated concurrently. The
 * runtime makes no guarantee on the ordering of completion of
 * executions. If it's important to the application, the application
 * should enforce the ordering by using
 * {@link ANeuralNetworksEvent_wait}.
 *
 * ANeuralNetworksEvent_wait must be called to recuperate the resources used
 * by the execution.
 *
 * See {@link ANeuralNetworksExecution} for information on multithreaded usage.
 *
 * @param execution The execution to be scheduled and executed.
 * @param event The event that will be signaled on completion. event is set to
 *              NULL if there's an error.
 *
 * @return ANEURALNETWORKS_NO_ERROR if successful.
 */
int ANeuralNetworksExecution_startCompute(ANeuralNetworksExecution* execution,
                                          ANeuralNetworksEvent** event);

/**
 * Waits until the execution completes.
 *
 * More than one thread can wait on an event. When the execution completes,
 * all threads will be released.
 *
 * See {@link ANeuralNetworksExecution} for information on multithreaded usage.
 *
 * @return ANEURALNETWORKS_NO_ERROR if the execution completed normally.
 */
int ANeuralNetworksEvent_wait(ANeuralNetworksEvent* event);

/**
 * Destroys the event.
 *
 * See {@link ANeuralNetworksExecution} for information on multithreaded usage.
 */
void ANeuralNetworksEvent_free(ANeuralNetworksEvent* event);

__END_DECLS

#endif  //  __ANDROID_API__ >= 27

#endif  // ANDROID_ML_NN_RUNTIME_NEURAL_NETWORKS_H

/** @} */