/*
 * 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.
 */

package android.hardware.neuralnetworks@1.0;

/**
 * Operand types.
 *
 * The type of an operand in a model.
 *
 * Types prefaced with TENSOR_* must be used for tensor data (i.e., tensors
 * with at least one dimension). Types not prefaced by TENSOR_* represent
 * scalar values and must have no dimensions.
 */
enum OperandType : int32_t {
    /**
     * The following entries are used to declare scalars.
     */
    FLOAT32             = 0,
    INT32               = 1,
    UINT32              = 2,

    /**
     * The following entries are used to declare tensors.
     */
    TENSOR_FLOAT32      = 3,
    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
     * - zero_value: a 32 bit integer
     *
     * The formula is:
     * real_value = (integer_value - zero_value) * scale.
     */
    TENSOR_QUANT8_ASYMM = 5,

    /**
     * The following entries are OEM specific operand types.
     */
    OEM                 = 10000,
    TENSOR_OEM_BYTE     = 10001,
};

/**
 * Operation types.
 *
 * The type of an operation in a model.
 */
enum OperationType : int32_t {
    /**
     * Adds two tensors, elment-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 OperandType::TENSOR_FLOAT32}
     * 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 FusedActivationFunc} values.
     *    Specifies the activation to invoke on the result of each addition.
     *
     * Ouputs:
     * 0: The sum, a tensor of the same type as input0.
     */
    ADD = 0,

    /**
     * Performs a 2-D average pooling operation.
     *
     * The output dimensions are functions of the filter dimensions, stride, and padding.
     *
     * The values in output Tensor is computed as:
     *     output[batch, row, col, channel] =
     *         sum_{i, j}(input[batch, row + i, col + j, channel]) / sum(1)
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
     * 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 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 output stride in the ‘width’ dimension.
     * 6: An INT32 value, specifying the output stride 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 FusedActivationFunc} values.
     *    Specifies the activation to invoke on the result of each addition.
     *
     * Ouputs:
     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
     */
    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 OperandType::TENSOR_FLOAT32}
     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
     * Supported tensor rank: up to 4
     *
     * Inputs:
     * 0 ~ n: The list on n input tensors, of shape [D0, D1, ..., Daxis(i), ..., Dm]
     * n+1: An INT32 value, specifying the concatenation axis.
     * n+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.
     *
     * Ouputs:
     * 0: The output, a tensor of the same type as the input tensors.
          The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
     */
    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 output Tensor is 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 OperandType::TENSOR_FLOAT32}
     *                         {@link OperandType::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: 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 OperandType::TENSOR_FLOAT32} type, the bias should
     *    also be of {@link OperandType::TENSOR_FLOAT32}.
     *    For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
     *    should be of {@link OperandType::TENSOR_INT32}.
     * 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 output stride in the ‘width’ dimension.
     * 8: An INT32 value, specifying the output stride in the ‘height’ dimension.
     * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
     *    Specifies the activation to invoke on the result of each addition.
     *
     * Ouputs:
     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out].
     */
    CONV_2D = 3,

    /**
     * Performs an depthwise 2-D convolution operation.
     *
     * Given an input tensor of shape [batches, height, width, depth_in] and a filter
     * tensor of shape [depth_out, filter_height, filter_width, depth_in] containing
     * in_channels 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 output Tensor is 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[di, dj, k, q]
     *         )
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     *                         {@link OperandType::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: 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 OperandType::TENSOR_FLOAT32} type, the bias should
     *    also be of {@link OperandType::TENSOR_FLOAT32}.
     *    For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
     *    should be of {@link OperandType::TENSOR_INT32}.
     * 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 output stride in the ‘width’ dimension.
     * 8: An INT32 value, specifying the output stride in the ‘height’ dimension.
     * 9: An INT32 value, specifying the depthwise multiplier.
     * 10: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
     *    Specifies the activation to invoke on the result of each addition.
     *
     * Ouputs:
     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out].
     */
    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 OperandType::TENSOR_FLOAT32}
     *                         {@link OperandType::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.
     *
     * Ouputs:
     * 0: The output 4-D tensor, of shape [batch, height*block_size, width*block_size,
     *    depth/(block_size*block_size)].
     */
    DEPTH_TO_SPACE = 5,

    /**
     * Dequantizes the input tensor.
     *
     * The formula is:
     *     output = (input - zero_value) * scale.
     *
     * Supported tensor types: {@link OperandType::TENSOR_QUANT8_ASYMM}
     * Supported tensor rank: up to 4
     *
     * Inputs:
     * 0: A tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}.
     *
     * Ouputs:
     * 0: The output tensor of same shape as input0, but with type
          {@link OperandType::TENSOR_FLOAT32}.
     */
    DEQUANTIZE = 6,

    /**
     * Looks up items from a given tensor.
     *
     * Each item in the output is a raw copy of the corresponding item in
     * the input “values”. If the the given “lookup” indices are out of bounds,
     * the op will fail and an error will be reported.
     *
     * Inputs:
     * * 0: Values. An n-D tensor of any type X (where n >= 2). E.g., if n is 2,
     *      then the shape would be [lookup_dimension, values_dimension], where
     *      “lookup_dimension” corresponds to the indexing dimension in the lookup
     *      table, and “values_dimension” to the contents.
     * * 1: Lookups. An 1-D tensor of type T, of shape [lookup_size], where
     *      “lookup_size” is the number of elements to look for, and each entry
     *      corresponds to the first dimension of the “values” tensor.
     *
     * Output:
     * * 0: A n-D tensor of type X and the same rank and shape as the “values”
     *      tensor, except for the first dimension which has size “lookup_size”.
     */
    EMBEDDING_LOOKUP = 7,

    /**
     * Computes element-wise floor() on the input tensor.
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     * Supported tensor rank: up to 4
     *
     * Inputs:
     * 0: A tensor.
     *
     * Ouputs:
     * 0: The output, a tensor of the same type and dimensions as input0.
     */
    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 OperandType::TENSOR_FLOAT32}
     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
     * Supported tensor rank: up to 4.
     *
     * Inputs:
     * 0: A tensor, specifying the input. If rank is greater than 2, then it gets flattened to
     *    a 2-D Tensor. The 2-D Tensor is handled as if dimensions corresponded to shape
     *    [batch_size, input_size], where “batch_size” corresponds to the batching dimension,
     *    and “input_size” is the size of the input.
     * 1: A 2-D tensor, specifying the weights, of shape [num_units, input_size], where “num_units”
     *    corresponds to the number of output nodes.
     * 2: A 1-D tensor, of shape [num_units], specifying the bias.
     *    For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
     *    also be of {@link OperandType::TENSOR_FLOAT32}.
     *    For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
     *    should be of {@link OperandType::TENSOR_INT32}.
     * 3: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
     *    Specifies the activation to invoke on the result of each addition.
     *
     * Ouputs:
     * 0: The output tensor, of shape [batch_size, num_units].
     */
    FULLY_CONNECTED = 9,

    /**
     * Looks up values of a hash table with given keys.
     *
     * Inputs:
     * * 0: Lookups. A 1-D int32 tensor with shape [ k ].
     * * 1: Keys. A 1-D int32 tensor with shape [ n ], *MUST* be sorted in
     *      ascending order.
     * * 2: Values. A tensor with shape [ n … ].
     *
     * Outputs:
     * * 0: Output. A tensor with shape [ k …].
     * * 1: Hits. A uint8 tensor with shape [ k ] indicates whether the lookup
     *      hits or not.
     */
    HASHTABLE_LOOKUP = 10,

    /**
     * Applies L2 normalization along a the depth dimension.
     *
     * The values in output Tensor is computed as:
     *     output[batch, row, col, channel] =
     *         input[batch, row, col, channel] /
     *         sqrt(sum_{c} pow(input[batch, row, col, c], 2))
     *
     * For x with more dimensions, independently normalizes each 1-D slice along dimension dim.
     *
     * Supported tensor types: {@link OperandType::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.
     *
     * Ouputs:
     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
     */
    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 output Tensor is 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 OperandType::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 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 output stride in the ‘width’ dimension.
     * 6: An INT32 value, specifying the output stride 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 FusedActivationFunc} values.
     *    Specifies the activation to invoke on the result of each addition.
     *
     * Ouputs:
     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
     */
    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.
     *
     * In details:
     *     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 OperandType::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.
     *
     * Ouputs:
     * 0: The output tensor of same shape as input0.
     */
    LOCAL_RESPONSE_NORMALIZATION = 13,

    /**
     * Computes sigmoid activation on the input tensor element-wise.
     *
     * In details:
     *     output = 1 / (1 + exp(-input))
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
     * Supported tensor rank: up to 4.
     *
     * Inputs:
     * 0: A tensor, specifying the input.
     *
     * Ouputs:
     * 0: The output tensor of same shape as input0.
     */
    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.
     */
    LSH_PROJECTION = 15,

    /**
     * Long short-term memory unit (LSTM) recurrent network layer.
     *
     * The default non-peephole implementation is based on:
     * http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
     * S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural
     * Computation, 9(8):1735-1780, 1997.
     *
     * The peephole implementation 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.
     *
     * The coupling of input and forget gate (CIFG) is based on:
     * http://arxiv.org/pdf/1503.04069.pdf
     * Greff et al. "LSTM: A Search Space Odyssey"
     *
     * The class has the following independently optional inputs:
     * * If input gate (if CIFG): “input_to_forget_weights”,
     *   “recurrent_to_input_weights”, “cell_to_input_weights”, “input_gate_bias”.
     * * If no peephole connections: “cell_to_input_weights”,
     *   “cell_to_forget_weights”, “cell_to_output_weights”.
     * * If no projection layer: “projection_weights” and “projection_bias”.
     * * If no projection bias: “projection_bias”.
     *
     * Supported tensor types:
     * * {@link OperandType::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: input_to_input_weights.
     *      A 2-D tensor of type T, of shape [num_units, input_size], where
     *      “num_units” corresponds to the number of cell units.
     * * 2: input_to_forget_weights.
     *      A 2-D tensor of type T, of shape [num_units, input_size].
     * * 3: input_to_cell_weights.
     *      A 2-D tensor of type T, of shape [num_units, input_size].
     * * 4: input_to_output_weights.
     *      A 2-D tensor of type T, of shape [num_units, input_size].
     * * 5: recurrent_to_input_weights.
     *      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: recurrent_to_forget_weights.
     *      A 2-D tensor of type T, of shape [num_units, output_size].
     * * 7: recurrent_to_cell_weights.
     *      A 2-D tensor of type T, of shape [num_units, output_size].
     * * 8: recurrent_to_output_weights.
     *      A 2-D tensor of type T, of shape [num_units, output_size].
     * * 9: cell_to_input_weights.
     *      A 1-D tensor of type T, of shape [num_units].
     * * 10:cell_to_forget_weights.
     *      A 1-D tensor of type T, of shape [num_units].
     * * 11:cell_to_output_weights.
     *      A 1-D tensor of type T, of shape [num_units].
     * * 12:input_gate_bias.
     *      A 1-D tensor of type T, of shape [num_units].
     * * 13:forget_gate_bias.
     *      A 1-D tensor of type T, of shape [num_units].
     * * 14:cell_bias.
     *      A 1-D tensor of type T, of shape [num_units].
     * * 15:output_gate_bias.
     *      A 1-D tensor of type T, of shape [num_units].
     * * 16:projection_weights.
     *      A 2-D tensor of type T, of shape [output_size, num_units].
     * * 17:projection_bias.
     *      A 1-D tensor of type T, of shape [output_size].
     *
     * Parameters:
     * * 18:fused_activation_function.
     *      An (optional) ActivationFunctionType indicating the activation
     *      function.
     *      If “NONE” is specified then it results in a linear activation.
     * * 19:cell_clip.
     *      A clipping threshold for the cell state, such that values are bound
     *      within [-cell_clip, cell_clip]. If set to 0.0 then clipping is
     *      disabled.
     * * 20:proj_clip.
     *      A clipping threshold for the output from the projection layer, such
     *      that values are bound within [-proj_clip, proj_clip]. If set to 0.0
     *      then clipping is disabled.
     *
     * Outputs:
     * * 0: scratch_buffer.
     *      A 3-D tensor of type T, of shape [batch_size, num_cell, 4].
     * * 1: output_state.
     *      A 2-D tensor of type T, of shape [batch_size, output_size].
     * * 2: cell_state.
     *      A 2-D tensor of type T, of shape [batch_size, num_units].
     * * 3: output.
     *      A 2-D tensor of type T, of shape [batch_size, output_size]. This is
     *      effectively the same as the current “output_state” value.
     */
    LSTM = 16,

    /**
     * Performs an 2-D max pooling operation.
     *
     * The output dimensions are functions of the filter dimensions, stride, and padding.
     *
     * The values in output Tensor is computed as:
     *     output[batch, row, col, channel] =
     *         max_{i, j} (input[batch, row + i, col + j, channel])
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
     * 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 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 output stride in the ‘width’ dimension.
     * 6: An INT32 value, specifying the output stride 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 FusedActivationFunc} values.
     *    Specifies the activation to invoke on the result of each addition.
     *
     * Ouputs:
     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
     */
    MAX_POOL_2D = 17,

    /**
     * Multiplies two tensors, elment-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 OperandType::TENSOR_FLOAT32}
     * 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 FusedActivationFunc} values.
     *    Specifies the activation to invoke on the result of each addition.
     *
     * Ouputs:
     * 0: The product, a tensor of the same type as input0.
     */
    MUL = 18,

    /**
     * Computes rectified linear activation on the input tensor element-wise.
     *
     * In details:
     *     output = max(0, input)
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
     * Supported tensor rank: up to 4.
     *
     * Inputs:
     * 0: A tensor, specifying the input.
     *
     * Ouputs:
     * 0: The output tensor of same shape as input0.
     */
    RELU = 19,

    /**
     * Computes rectified linear 1 activation on the input tensor element-wise.
     *
     * In details:
     *     output = min(1.f, max(-1.f, input))
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
     * Supported tensor rank: up to 4.
     *
     * Inputs:
     * 0: A tensor, specifying the input.
     *
     * Ouputs:
     * 0: The output tensor of same shape as input0.
     */
    RELU1 = 20,

    /**
     * Computes rectified linear 6 activation on the input tensor element-wise.
     *
     * In details:
     *     output = min(6, max(0, input))
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
     * Supported tensor rank: up to 4.
     *
     * Inputs:
     * 0: A tensor, specifying the input.
     *
     * Ouputs:
     * 0: The output tensor of same shape as input0.
     */
    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 OperandType::TENSOR_FLOAT32}
     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
     * Supported tensor rank: up to 4.
     *
     * Inputs:
     * 0: A tensor, specifying the tensor to be reshaped.
     * 1: A 1-D tensor of type {@link OperandType::TENSOR_INT32}, defining the shape
     *    of the output tensor. The number of elements implied by shape must be the same
     *    as the number of elements in the input tensor.
     *
     * Ouputs:
     * 0: The output tensor, of shape specified by the input shape.
     */
    RESHAPE = 22,

    /**
     * Resizes images to given size using the bilinear interpretation.
     *
     * Resized images will be distorted if their original aspect ratio is not the same as input.
     *
     * Supported tensor types: {@link OperandType::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 width of the output tensor.
     * 2: An INT32 value, specifying the output height of the output tensor.
     *
     * Ouputs:
     * 0: The output 4-D tensor, of shape [batches, new_height, new_width, depth].
     */
    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:
     * * {@link OperandType::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].
     *
     *    For FLOAT32 input tensor, bias must also be FLOAT32.
     *    For UINT8 input tensor, bias must be INT32.
     *
     * Parameters
     * * 4: fused_activation_function.
     *      An (optional) ActivationFunctionType indicating the activation
     *      function. If “NONE” is specified then it results in a linear
     *      activation.
     *
     * * 5: Hidden state.
     *      A 2-D tensor of type T, of shape [batch_size, num_units].
     *
     * Outputs:
     * * 0: output.
     *      A 2-D tensor of type T, of shape [batch_size, num_units]. This is
     *      effectively the same as the current state value.
     */
    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.
     *
     * In details:
     *     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 OperandType::TENSOR_FLOAT32}
     *                         {@link OperandType::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 scaling factor for the exponent, beta.
     *
     * Ouputs:
     * 0: The output tensor of same shape as input0.
     */
    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 OperandType::TENSOR_FLOAT32}
     *                         {@link OperandType::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.
     *
     * Ouputs:
     * 0: The output 4-D tensor, of shape [batch, height/block_size, width/block_size,
     *    depth*block_size*block_size].
     */
    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, "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:
     * * {@link OperandType::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.
     *      A optional 1-D tensor of type T, of shape [num_units].
     *
     *    For FLOAT32 input tensor, bias must also be FLOAT32.
     *    For UINT8 input tensor, bias must be INT32.
     *
     * Parameters:
     * * 4: rank.
     *      The rank of the SVD approximation.
     * * 5: fused_activation_function.
     *      An (optional) ActivationFunctionType indicating the activation function.
     *      If “NONE” is specified then it results in a linear activation.
     *
     * Outputs:
     * * 0: state.
     *      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].
     */
    SVDF = 27,

    /**
     * Computes hyperbolic tangent of input tensor element-wise.
     *
     * In details:
     *     output = tanh(input)
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     * Supported tensor rank: up to 4.
     *
     * Inputs:
     * 0: A tensor, specifying the input.
     *
     * Ouputs:
     * 0: The output tensor of same shape as input0.
     */
    TANH = 28,

    /**
     * OEM specific operation.
     *
     * This operation is OEM specific. It should only be used for OEM applications.
     */
    OEM_OPERATION = 10000,
};

/**
 * Fused activation function types.
 */
enum FusedActivationFunc : int32_t {
    NONE  = 0,
    RELU  = 1,
    RELU1 = 2,
    RELU6 = 3,
};

/**
 * How an operand is used.
 */
enum OperandLifeTime : int32_t {
    /**
     * The operand is internal to the model.  It's created by an operation
     * and consumed by other operations.
     */
    TEMPORARY_VARIABLE,

    /**
     * The operand is an input of the model. An operand can't be both
     * input and output of a model.
     */
    MODEL_INPUT,

    /**
     * The operand is an output of the model.
     */
    MODEL_OUTPUT,

    /**
     * The operand is a constant found in Model.operandValues.
     */
    CONSTANT_COPY,

    /**
     * The operand is a constant that was specified via a Memory object.
     */
    CONSTANT_REFERENCE,

    /**
     * The operand does not have a value. This is valid only for optional arguments
     * of operations.
     */
    NO_VALUE,
};

/**
 * Status of a device.
 */
enum DeviceStatus : int32_t {
    AVAILABLE,
    BUSY,
    OFFLINE,
    UNKNOWN,
};

/**
 * Performance information for the reference workload.
 *
 * Used by a driver to report its performance characteristics.
 */
struct PerformanceInfo {
    /**
     * Ratio of the time taken by the driver to execute the
     * workload compared to the time the CPU would take for the
     * same workload. A lower number is better.
     */
    float execTime;

    /**
     * Ratio of the energy used by the driver compared to what
     * the CPU would use for doing the same workload. A lower number
     * is better.
     */
    float powerUsage;
};

/**
 * The capabilities of a driver.
 */
struct Capabilities {
    /**
     * Driver performance when operating on float32 data.
     */
    PerformanceInfo float32Performance;

    /**
     * Driver performance when operating on asymmetric 8-bit quantized data.
     */
    PerformanceInfo quantized8Performance;
};

/**
 * Describes the location of a data object.
 */
struct DataLocation {
    /**
     * The index of the memory pool where this location is found.
     */
    uint32_t poolIndex;

    /**
     * Offset in bytes from the start of the pool.
     */
    uint32_t offset;

    /**
     * The length of the data in bytes.
     */
    uint32_t length;
};

/**
 * Describes one operand of the model's graph.
 */
struct Operand {
    /**
     * Data type of the operand.
     */
    OperandType type;

    /**
     * Dimensions of the operand.
     */
    vec<uint32_t> dimensions;

    /**
     * The number of operations that use this operand as input.
     */
    uint32_t numberOfConsumers;

    /**
     * Quantized scale of the operand.
     *
     * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM or
     * TENSOR_INT32.
     */
    float scale;

    /**
     * Quantized zero-point offset of the operand.
     *
     * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM.
     */
    int32_t zeroPoint;

    /**
     * How the operand is used.
     */
    OperandLifeTime lifetime;

    /**
     * Where to find the data for this operand.
     * If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, MODEL_OUTPUT, or NO_VALUE:
     * - All the fields will be 0.
     * If the lifetime is CONSTANT_COPY:
     * - location.poolIndex is 0.
     * - location.offset is the offset in bytes into Model.operandValues.
     * - location.length is set.
     * If the lifetime is CONSTANT_REFERENCE:
     * - location.poolIndex is set.
     * - location.offset is the offset in bytes into the specified pool.
     * - location.length is set.
     */
    DataLocation location;
};

/**
 * Describes one operation of the model's graph.
 */
struct Operation {
    /**
     * The operation type.
     */
    OperationType type;

    /**
     * Describes the table that contains the indexes of the inputs of the
     * operation. The offset is the index in the operandIndexes table.
     */
    vec<uint32_t> inputs;

    /**
     * Describes the table that contains the indexes of the outputs of the
     * operation. The offset is the index in the operandIndexes table.
     */
    vec<uint32_t> outputs;
};

/**
 * A Neural Network Model.
 *
 * This includes not only the execution graph, but also constant data such as
 * weights or scalars added at construction time. The only information that
 * might not be known is the shape of the input tensors.
 */
struct Model {
    /**
     * All operands included in the model.
     */
    vec<Operand> operands;

    /**
     * All operations included in the model.
     *
     * The operations are sorted into execution order.
     */
    vec<Operation> operations;

    /**
     * Input indexes of the model.
     *
     * Each value corresponds to the index of the operand in "operands".
     */
    vec<uint32_t> inputIndexes;

    /**
     * Output indexes of the model.
     *
     * Each value corresponds to the index of the operand in "operands".
     */
    vec<uint32_t> outputIndexes;

    /**
     * A byte buffer containing operand data that were copied into the model.
     *
     * An operand's value must be located here if and only if Operand::lifetime
     * equals OperandLifeTime::CONSTANT_COPY.
     */
    vec<uint8_t> operandValues;

    /**
     * A collection of shared memory pools containing operand data that were
     * registered by the model.
     *
     * An operand's value must be located here if and only if Operand::lifetime
     * equals OperandLifeTime::CONSTANT_REFERENCE.
     */
    vec<memory> pools;
};

/**
 * Metadata information specifying the location of the input or output data and
 * any updates to the input or output operand.
 */
struct RequestArgument {
    /**
     * If true, the argument does not have a value. This can be used for operations
     * that take optional arguments. If true, the fields of location are set to 0 and
     * the dimensions vector is left empty.
     */
    bool hasNoValue;

    /**
     * The location within one of the memory pools passed in the Request.
     */
    DataLocation location;

    /**
     * Updated dimension information.
     *
     * If dimensions.size() > 0, dimension information was provided along with the
     * argument.  This can be the case for models that accept inputs of varying size.
     * This can't change the rank, just the value of the dimensions that were
     * unspecified in the model.
     */
    vec<uint32_t> dimensions;
};

/**
 * Inputs to be sent to and outputs to be retrieved from a prepared model.
 *
 * A Request serves two primary tasks:
 * 1) Provides the input and output data to be used when executing the model.
 * 2) Specifies any updates to the input operand metadata that were left
 *    unspecified at model preparation time.
 */
struct Request {
    /**
     * Input data and information to be used in the execution of a prepared
     * model.
     *
     * The index of the input corresponds to the index in Model.inputIndexes.
     *   E.g., input[i] corresponds to Model.inputIndexes[i].
     */
    vec<RequestArgument> inputs;

    /**
     * Output data and information to be used in the execution of a prepared
     * model.
     *
     * The index of the output corresponds to the index in Model.outputIndexes.
     *   E.g., output[i] corresponds to Model.outputIndexes[i].
     */
    vec<RequestArgument> outputs;

    /**
     * A collection of shared memory pools containing operand data for both the
     * inputs and the outputs to a model.
     */
    vec<memory> pools;
};

/**
 * Return status of a function.
 */
enum ErrorStatus : int32_t {
    NONE,
    DEVICE_UNAVAILABLE,
    GENERAL_FAILURE,
    OUTPUT_INSUFFICIENT_SIZE,
    INVALID_ARGUMENT,
};