/*
 * SPDX-License-Identifier: Apache-2.0
 */


#include <cmath>
#include "onnx/defs/function.h"
#include "onnx/defs/schema.h"

namespace ONNX_NAMESPACE {

static const char* Dropout_ver12_doc = R"DOC(
Dropout takes an input floating-point tensor, an optional input ratio (floating-point scalar) and an optional input training_mode (boolean scalar). It produces two tensor outputs,
output (floating-point tensor) and mask (optional `Tensor<bool>`). If `training_mode` is true then the output Y will be a random dropout;
Note that this Dropout scales the masked input data by the following equation, so to convert the trained model into inference mode,
the user can simply not pass `training_mode` input or set it to false.
```
output = scale * data * mask,
```
where
```
scale = 1. / (1. - ratio).
```
)DOC";

ONNX_OPERATOR_SET_SCHEMA(
    Dropout,
    12,
    OpSchema()
        .SetDoc(GET_OP_DOC_STR(
            std::string(Dropout_ver12_doc) + GenerateOptionalArgumentsDoc()))
        .Attr(
            "seed",
            "(Optional) Seed to the random generator, if not specified we will auto generate one.",
            AttributeProto::INT,
            OPTIONAL_VALUE)
        .Input(0, "data", "The input data as Tensor.", "T")
        .Input(
            1,
            "ratio",
            "The ratio of random dropout, with value in [0, 1). If this input was not set, "
            "or if it was set to 0, the output would be a simple copy of the input. "
            "If it's non-zero, output will be a random dropout of the scaled input, which is typically "
            "the case during training. It is an optional value, if not specified it will default to 0.5.",
            "T1",
            OpSchema::Optional)
        .Input(
            2,
            "training_mode",
            "If set to true then it indicates dropout is being used for training. It is an optional value hence unless "
            "specified explicitly, it is false. If it is false, ratio is ignored and the operation mimics inference mode where "
            "nothing will be dropped from the input data and if mask is requested as output it will contain all ones.",
            "T2",
            OpSchema::Optional)
        .Output(0, "output", "The output.", "T")
        .Output(1, "mask", "The output mask.", "T2", OpSchema::Optional)
        .TypeConstraint(
            "T",
            {"tensor(float16)", "tensor(float)", "tensor(double)"},
            "Constrain input and output types to float tensors.")
        .TypeConstraint(
            "T1",
            {"tensor(float16)", "tensor(float)", "tensor(double)"},
            "Constrain input 'ratio' types to float tensors.")
        .TypeConstraint(
            "T2",
            {"tensor(bool)"},
            "Constrain output 'mask' types to boolean tensors.")
        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
          propagateElemTypeFromInputToOutput(ctx, 0, 0);
          if (hasInputShape(ctx, 0)) {
            propagateShapeFromInputToOutput(ctx, 0, 0);
          }

          if (ctx.getNumInputs() > 1 && hasInputShape(ctx, 1)) {
            auto& ratio_input_shape = getInputShape(ctx, 1);
            if (static_cast<int>(ratio_input_shape.dim_size()) != 0) {
              fail_shape_inference("Ratio of Dropout must be a scalar.");
            }
          }

          if (ctx.getNumInputs() > 2 && hasInputShape(ctx, 2)) {
            auto& training_mode_input_shape = getInputShape(ctx, 2);
            if (static_cast<int>(training_mode_input_shape.dim_size()) != 0) {
              fail_shape_inference(
                  "training_mode of Dropout must be a scalar.");
            }
          }

          if (ctx.getNumOutputs() == 2) {
            updateOutputElemType(ctx, 1, TensorProto::BOOL);
            if (hasNInputShapes(ctx, 1)) {
              propagateShapeFromInputToOutput(ctx, 0, 1);
            }
          }
        }));

static const char* Flatten_ver11_doc = R"DOC(
Flattens the input tensor into a 2D matrix. If input tensor has shape
(d_0, d_1, ... d_n) then the output will have shape
(d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn).
)DOC";

ONNX_OPERATOR_SET_SCHEMA(
    Flatten,
    11,
    OpSchema()
        .SetDoc(Flatten_ver11_doc)
        .Input(0, "input", "A tensor of rank >= axis.", "T")
        .Output(
            0,
            "output",
            "A 2D tensor with the contents of the input tensor, "
            "with input dimensions up to axis flattened to the outer dimension "
            "of the output and remaining input dimensions flattened into the inner "
            "dimension of the output.",
            "T")
        .TypeConstraint(
            "T",
            OpSchema::all_tensor_types(),
            "Constrain input and output to all tensor types.")
        .Attr(
            "axis",
            "Indicate up to which input dimensions "
            "(exclusive) should be flattened to the outer dimension of the output. "
            "The value for axis must be in the range [-r, r], where r is the rank of the input tensor. "
            "Negative value means counting dimensions from the back. "
            "When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), "
            "where the shape of the input tensor is (d_0, d_1, ... d_n). ",
            AttributeProto::INT,
            static_cast<int64_t>(1))
        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
          propagateElemTypeFromInputToOutput(ctx, 0, 0);
          if (!hasInputShape(ctx, 0))
            return;
          auto& input_shape = getInputShape(ctx, 0);
          int rank = static_cast<int>(input_shape.dim_size());
          int axis = static_cast<int>(getAttribute(ctx, "axis", 1));
          if (axis < 0) {
            axis += rank;
          }
          if (axis > rank || axis < 0) {
            fail_shape_inference(
                "Invalid value(", axis, ") for attribute 'axis'");
          }
          // TODO: is the operation defined for input-rank < 2?
          updateOutputShape(
              ctx,
              0,
              {multiplyDims(input_shape, 0, axis),
               multiplyDims(input_shape, axis, rank)});
        }));

static const char* LRN_ver1_doc = R"DOC(
Local Response Normalization proposed in the [AlexNet paper](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf).
It normalizes over local input regions.
The local region is defined across the channels. For an element X[n, c, d1, ..., dk] in a tensor
of shape (N x C x D1 x D2, ..., Dk), its region is
{X[n, i, d1, ..., dk] | max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2))}.

square_sum[n, c, d1, ..., dk] = sum(X[n, i, d1, ..., dk] ^ 2),
where max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2)).

Y[n, c, d1, ..., dk] = X[n, c, d1, ..., dk] / (bias + alpha / size * square_sum[n, c, d1, ..., dk] ) ^ beta
)DOC";

ONNX_OPERATOR_SET_SCHEMA(
    LRN,
    1,
    OpSchema()
        .Attr("size", "The number of channels to sum over", AttributeProto::INT)
        .Attr("alpha", "Scaling parameter.", AttributeProto::FLOAT, 0.0001f)
        .Attr("beta", "The exponent.", AttributeProto::FLOAT, 0.75f)
        .Attr("bias", "", AttributeProto::FLOAT, 1.0f)
        .Input(
            0,
            "X",
            "Input data tensor from the previous operator; "
            "dimensions for image case are (N x C x H x W), "
            "where N is the batch size, C is the number of "
            "channels, and H and W are the height and the "
            "width of the data. For non image case, the "
            "dimensions are in the form of "
            "(N x C x D1 x D2 ... Dn), where N is the batch "
            "size. Optionally, if dimension denotation is "
            "in effect, the operation expects the input "
            "data tensor to arrive with the dimension denotation "
            "of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].",
            "T")
        .Output(
            0,
            "Y",
            "Output tensor, which has the shape and type as input tensor",
            "T")
        .TypeConstraint(
            "T",
            {"tensor(float16)", "tensor(float)", "tensor(double)"},
            "Constrain input and output "
            " types to float tensors.")
        .SetDoc(LRN_ver1_doc)
        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));

static const char* mvn_ver9_doc = R"DOC(
      A MeanVarianceNormalization Function: Perform mean variance normalization
      on the input tensor X using formula: <br/> ``` (X-EX)/sqrt(E(X-EX)^2) ```
)DOC";

static std::vector<int64_t> mvn_default_axes = {0, 2, 3};

ONNX_OPERATOR_SET_SCHEMA(
    MeanVarianceNormalization,
    9,
    OpSchema()
        .SetDoc(mvn_ver9_doc)
        .Input(0, "X", "Input tensor", "T")
        .Output(0, "Y", "Output tensor", "T")
        .Attr(
            "axes",
            "A list of integers, along which to reduce. The default is to "
            "caculate along axes [0,2,3] for calculating mean and variance "
            "along each channel. Two variables with the same C-coordinate "
            "are associated with the same mean and variance.",
            AttributeProto::INTS,
            mvn_default_axes)
        .TypeConstraint(
            "T",
            {"tensor(float16)", "tensor(float)", "tensor(double)"},
            "Constrain input and output types to all numeric tensors.")
        .FunctionBody(FunctionBodyHelper::BuildNodes(
            {// nodes: {outputs, op, inputs, attributes}
             FunctionBodyHelper::Const<float>("Exponent", 2.0f),
             FunctionBodyHelper::Const<float>("Epsilon", float(1e-9)),
             {{"X_RM"},
              "ReduceMean",
              {"X"},
              {MakeRefAttribute("axes", AttributeProto::INTS)}},
             {{"EX_squared"}, "Pow", {"X_RM", "Exponent"}},
             {{"X_squared"}, "Pow", {"X", "Exponent"}},
             {{"E_Xsquared"},
              "ReduceMean",
              {"X_squared"},
              {MakeRefAttribute("axes", AttributeProto::INTS)}},
             {{"Variance"}, "Sub", {"E_Xsquared", "EX_squared"}},
             {{"STD"}, "Sqrt", {"Variance"}},
             {{"X_variance"}, "Sub", {"X", "X_RM"}},
             {{"Processed_STD"}, "Add", {"STD", "Epsilon"}},
             {{"Y"}, "Div", {"X_variance", "Processed_STD"}}})));

const char* pads_doc2 =
    "Padding for the beginning and ending along each spatial axis, it can take any value greater "
    "than or equal to 0. The value represent the number of pixels added to the beginning "
    "and end part of the corresponding axis. `pads` format should be as follow "
    "[x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels "
    "added at the beginning of axis `i` and xi_end, the number of pixels added at "
    "the end of axis `i`. This attribute cannot be used simultaneously with "
    "auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.";
const char* auto_pad_doc2 =
    "auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where "
    "default value is NOTSET, which means explicit padding is used. "
    "SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input."
    "In case of odd number add the extra padding at the end for SAME_UPPER and at the "
    "beginning for SAME_LOWER. VALID mean no padding.";

void convPoolShapeInference1(
    InferenceContext& ctx,
    bool use_dilation,
    bool require_kernel_shape,
    int input1Idx,
    int input2Idx) {
  // we need the first input shape for this inference.
  if (!hasInputShape(ctx, input1Idx)) {
    return;
  }

  // if kernel shape is an input (and not attribute)
  // we need the shape of the second input.
  if (!require_kernel_shape && !hasInputShape(ctx, input2Idx)) {
    return;
  }

  auto input_shape = ctx.getInputType(input1Idx)->tensor_type().shape();
  if (input_shape.dim_size() < 2) {
    fail_shape_inference("Input tensor must have atleast 2 dimensions");
  }

  // first dim is the batch axis and the next is the number of channels.
  size_t n_input_dims = static_cast<size_t>(input_shape.dim_size() - 2);

  // Only MaxPool and Conv support dilation. For
  // simplicity of the code, we just treat the rest of them as having all-1s
  // dilation.
  std::vector<int64_t> dilations;
  if (use_dilation && getRepeatedAttribute(ctx, "dilations", dilations)) {
    if (dilations.size() != n_input_dims) {
      fail_shape_inference("Attribute dilations has incorrect size");
    }
  } else {
    dilations.assign(n_input_dims, 1);
  }

  std::vector<int64_t> strides;
  if (getRepeatedAttribute(ctx, "strides", strides)) {
    if (strides.size() != n_input_dims) {
      fail_shape_inference("Attribute strides has incorrect size");
    }
  } else {
    strides.assign(n_input_dims, 1);
  }

  std::vector<int64_t> kernel_shape;
  if (getRepeatedAttribute(ctx, "kernel_shape", kernel_shape)) {
    if (kernel_shape.size() != n_input_dims) {
      fail_shape_inference("Attribute kernel_shape has incorrect size");
    }
  } else if (require_kernel_shape) {
    fail_shape_inference("Attribute kernel_shape must be specified");
  } else {
    auto second_input_shape =
        ctx.getInputType(input2Idx)->tensor_type().shape();
    for (int i = 2; i < second_input_shape.dim_size(); ++i) {
      if (!second_input_shape.dim(i).has_dim_value()) {
        return;
      }
      kernel_shape.push_back(second_input_shape.dim(i).dim_value());
    }
  }

  std::vector<int64_t> effective_kernel_shape = kernel_shape;
  for (int i = 0; i < static_cast<int>(kernel_shape.size()); i++) {
    // accounting for dilation, how big is the kernel in this dimension
    effective_kernel_shape[i] =
        (effective_kernel_shape[i] - 1) * dilations[i] + 1;
  }

  std::vector<int64_t> pads;
  if (getRepeatedAttribute(ctx, "pads", pads)) {
    if (pads.size() != n_input_dims * 2) {
      fail_shape_inference("Attribute pads has incorrect size");
    }
  } else {
    pads.assign(n_input_dims * 2, 0);
    const auto* auto_pad_attr = ctx.getAttribute("auto_pad");
    if ((nullptr != auto_pad_attr) && (auto_pad_attr->s() != "VALID")) {
      int input_dims_size = static_cast<int>(n_input_dims);
      for (int i = 0; i < input_dims_size; ++i) {
        int64_t residual = 0;
        int64_t stride = strides[i];
        if (stride > 1) {
          if (!input_shape.dim(2 + i).has_dim_value()) {
            continue;
          }
          residual = input_shape.dim(2 + i).dim_value();
          while (residual >= stride) {
            residual -= stride;
          }
        }
        int64_t total_pad = residual == 0
            ? effective_kernel_shape[i] - stride
            : effective_kernel_shape[i] - residual;
        if (total_pad < 0)
          total_pad = 0;
        int64_t half_pad_small = total_pad >> 1;
        int64_t half_pad_big = total_pad - half_pad_small;
        if (auto_pad_attr->s() == "SAME_UPPER") {
          pads[i] = half_pad_small;
          pads[i + input_dims_size] = half_pad_big;
        } else if (auto_pad_attr->s() == "SAME_LOWER") {
          pads[i] = half_pad_big;
          pads[i + input_dims_size] = half_pad_small;
        }
      }
    }
  }

  auto output_shape =
      ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();

  if (require_kernel_shape) {
    // add the first two dimensions from the input.
    *output_shape->add_dim() = input_shape.dim(0);
    *output_shape->add_dim() = input_shape.dim(1);
  } else {
    *output_shape->add_dim() = input_shape.dim(0);
    auto& second_input_shape = getInputShape(ctx, input2Idx);
    if (second_input_shape.dim_size() < 1) {
      fail_shape_inference("Second input tensor has wrong dimension");
    }
    *output_shape->add_dim() = second_input_shape.dim(0);
  }

  int kernel_shape_size = static_cast<int>(kernel_shape.size());
  for (int i = 0; i < kernel_shape_size; ++i) {
    auto newdim = output_shape->add_dim();
    if (!input_shape.dim(2 + i).has_dim_value()) {
      continue;
    }
    // how big is the input, including padding
    int64_t effective_input_size = input_shape.dim(2 + i).dim_value();
    effective_input_size += pads[i];
    effective_input_size += pads[i + kernel_shape_size];

    // default is floor mode .i.e. ceil_mode is set to 0
    auto ceil_mode = getAttribute(ctx, "ceil_mode", 0);

    // how many times we can move the kernel from it's initial position, based
    // on the stride
    int64_t strided_kernel_positions;

    if (ceil_mode == 1)
      strided_kernel_positions = (int64_t)(std::ceil(
          (effective_input_size - effective_kernel_shape[i]) /
          float(strides[i])));
    else
      strided_kernel_positions =
          (effective_input_size - effective_kernel_shape[i]) / strides[i];

    // add in the initial position
    newdim->set_dim_value(1 + strided_kernel_positions);
  }

  if (ctx.getNumOutputs() > 1) {
    // MaxPool with two outputs case.
    auto second_output_shape =
        ctx.getOutputType(1)->mutable_tensor_type()->mutable_shape();
    second_output_shape->CopyFrom(*output_shape);
  }
}

std::function<void(OpSchema&)> PoolOpSchemaGenerator_9(
    const char* name,
    const char* opName,
    const char* additionalDescription) {
  return [=](OpSchema& schema) {
    std::string doc;
    POPULATE_OP_DOC_STR(
        doc = R"DOC(
 {name} consumes an input tensor X and applies {opName} pooling across
 the tensor according to kernel sizes, stride sizes, and pad lengths.
 {opName} pooling consisting of computing the {opName} on all values of a
 subset of the input tensor according to the kernel size and downsampling the
 data into the output tensor Y for further processing. The output spatial shape will be following:
 ```
 output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1)

 * pad_shape[i] is sum of pads along axis i
 ```

 `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following:
 ```
 VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - kernel_spatial_shape[i] + 1) / strides_spatial_shape[i])
 SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])
 ```
 And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`:
 ```
 pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + kernel_spatial_shape[i] - input_spatial_shape[i]
 ```
 {additionalDescription}
 )DOC";
        ReplaceAll(doc, "{name}", name);
        ReplaceAll(doc, "{opName}", opName);
        ReplaceAll(doc, "{additionalDescription}", additionalDescription););
    schema.SetDoc(doc);
    schema.Attr(
        "kernel_shape",
        "The size of the kernel along each axis.",
        AttributeProto::INTS);
    schema.Attr(
        "strides",
        "Stride along each spatial axis.",
        AttributeProto::INTS,
        OPTIONAL_VALUE);
    schema.Attr(
        "auto_pad",
        auto_pad_doc2,
        AttributeProto::STRING,
        std::string("NOTSET"));
    schema.Attr("pads", pads_doc2, AttributeProto::INTS, OPTIONAL_VALUE);
    schema.Input(
        0,
        "X",
        "Input data tensor from the previous operator; "
        "dimensions for image case are (N x C x H x W), "
        "where N is the batch size, C is the number of "
        "channels, and H and W are the height and the "
        "width of the data. For non image case, the "
        "dimensions are in the form of "
        "(N x C x D1 x D2 ... Dn), where N is the batch "
        "size. Optionally, if dimension denotation is "
        "in effect, the operation expects the input "
        "data tensor to arrive with the dimension denotation "
        "of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].",
        "T");
    schema.Output(
        0,
        "Y",
        "Output data tensor from average or max pooling across "
        "the input tensor. Dimensions will vary based "
        "on various kernel, stride, and pad sizes. Floor value of "
        "the dimension is used",
        "T");
    schema.TypeConstraint(
        "T",
        {"tensor(float16)", "tensor(float)", "tensor(double)"},
        "Constrain input and output types to float tensors.");
    schema.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
      propagateElemTypeFromInputToOutput(ctx, 0, 0);
      if (ctx.getNumOutputs() > 1) {
        // MaxPool with two outputs case.
        auto output_type = ctx.getOutputType(1);
        if (output_type->value_case() == TypeProto::kTensorType ||
            output_type->value_case() == TypeProto::VALUE_NOT_SET) {
          output_type->mutable_tensor_type()->set_elem_type(TensorProto::INT64);
        }
      }
      convPoolShapeInference1(ctx, false, true, 0, 1);
    });
  };
}

std::function<void(OpSchema&)> PoolOpSchemaGenerator_10(
    const char* name,
    const char* opName,
    const char* additionalDescription,
    bool use_dilation,
    int opsetNum) {
  return [=](OpSchema& schema) {
    std::string doc;
    POPULATE_OP_DOC_STR(
        doc = R"DOC(
 {name} consumes an input tensor X and applies {opName} pooling across
 the tensor according to kernel sizes, stride sizes, and pad lengths.
 {opName} pooling consisting of computing the {opName} on all values of a
 subset of the input tensor according to the kernel size and downsampling the
 data into the output tensor Y for further processing. The output spatial shape will be following:
 ```
 output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1)
 ```
 or
 ```
 output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1)
 ```
 if ceil_mode is enabled

 ```
 * pad_shape[i] is sum of pads along axis i
 ```

 `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following:
 ```
 VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - {kernelSpatialShape} + 1) / strides_spatial_shape[i])
 SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])
 ```
 And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`:
 ```
 pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + {kernelSpatialShape} - input_spatial_shape[i]
 ```
 {additionalDescription}
 )DOC";
        ReplaceAll(doc, "{name}", name);
        ReplaceAll(doc, "{opName}", opName);
        ReplaceAll(doc, "{additionalDescription}", additionalDescription);
        ReplaceAll(
            doc,
            "{kernelSpatialShape}",
            use_dilation ? "((kernel_spatial_shape[i] - 1) * dilations[i] + 1)"
                         : "kernel_spatial_shape[i]"););
    schema.SetDoc(doc);
    schema.Attr(
        "kernel_shape",
        "The size of the kernel along each axis.",
        AttributeProto::INTS);
    schema.Attr(
        "strides",
        opsetNum == 11
            ? "Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis."
            : "Stride along each spatial axis.",
        AttributeProto::INTS,
        OPTIONAL_VALUE);
    schema.Attr(
        "auto_pad",
        auto_pad_doc2,
        AttributeProto::STRING,
        std::string("NOTSET"));
    schema.Attr("pads", pads_doc2, AttributeProto::INTS, OPTIONAL_VALUE);
    schema.Attr(
        "ceil_mode",
        "Whether to use ceil or floor (default) to compute the output shape.",
        AttributeProto::INT,
        static_cast<int64_t>(0));
    schema.Input(
        0,
        "X",
        "Input data tensor from the previous operator; "
        "dimensions for image case are (N x C x H x W), "
        "where N is the batch size, C is the number of "
        "channels, and H and W are the height and the "
        "width of the data. For non image case, the "
        "dimensions are in the form of "
        "(N x C x D1 x D2 ... Dn), where N is the batch "
        "size. Optionally, if dimension denotation is "
        "in effect, the operation expects the input "
        "data tensor to arrive with the dimension denotation "
        "of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].",
        "T");
    schema.Output(
        0,
        "Y",
        "Output data tensor from average or max pooling across "
        "the input tensor. Dimensions will vary based "
        "on various kernel, stride, and pad sizes. Floor value of "
        "the dimension is used",
        "T");
    schema.TypeConstraint(
        "T",
        {"tensor(float16)", "tensor(float)", "tensor(double)"},
        "Constrain input and output types to float tensors.");
    schema.TypeAndShapeInferenceFunction([use_dilation](InferenceContext& ctx) {
      propagateElemTypeFromInputToOutput(ctx, 0, 0);
      if (ctx.getNumOutputs() > 1) {
        // MaxPool with two outputs case.
        auto output_type = ctx.getOutputType(1);
        if (output_type->value_case() == TypeProto::kTensorType ||
            output_type->value_case() == TypeProto::VALUE_NOT_SET) {
          output_type->mutable_tensor_type()->set_elem_type(TensorProto::INT64);
        }
      }
      convPoolShapeInference1(ctx, use_dilation, true, 0, 1);
    });
  };
}

ONNX_OPERATOR_SET_SCHEMA(
    AveragePool,
    1,
    OpSchema().FillUsing(PoolOpSchemaGenerator_9(
        "AveragePool",
        "average",
        "The output of each pooling window is divided by the number of elements exclude pad.")));

ONNX_OPERATOR_SET_SCHEMA(
    AveragePool,
    7,
    OpSchema()
        .FillUsing(PoolOpSchemaGenerator_9(
            "AveragePool",
            "average",
            "The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero)."))
        .Attr(
            "count_include_pad",
            "Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.",
            AttributeProto::INT,
            static_cast<int64_t>(0)));

ONNX_OPERATOR_SET_SCHEMA(
    AveragePool,
    10,
    OpSchema()
        .FillUsing(PoolOpSchemaGenerator_10(
            "AveragePool",
            "average",
            "The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero).",
            false,
            10))
        .Attr(
            "count_include_pad",
            "Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.",
            AttributeProto::INT,
            static_cast<int64_t>(0)));

ONNX_OPERATOR_SET_SCHEMA(
    MaxPool,
    1,
    OpSchema().FillUsing(PoolOpSchemaGenerator_9(
        "MaxPool",
        "max",
        "The output of each pooling window is maximum number of elements exclude pad.")));

ONNX_OPERATOR_SET_SCHEMA(
    MaxPool,
    8,
    OpSchema()
        .FillUsing(PoolOpSchemaGenerator_9(
            "MaxPool",
            "max",
            "The output of each pooling window is maximum number of elements exclude pad."))
        .Attr(
            "storage_order",
            "The storage order of the tensor. 0 is row major, and 1 is column major.",
            AttributeProto::INT,
            static_cast<int64_t>(0))
        .Output(
            1,
            "Indices",
            "Indices tensor from max pooling across the input tensor. "
            "The dimensions of indices are the same as output tensor. "
            "The values in indices of are the indices of the selected values during pooling. "
            "The indices are computed as flatten 1-D tensor, "
            "and the indices do not consider padding. "
            "So the values in indices are in [0, N x C x D1 x ... x Dn).",
            "I",
            OpSchema::Optional)
        .TypeConstraint(
            "I",
            {"tensor(int64)"},
            "Constrain index tensor to int64"));

ONNX_OPERATOR_SET_SCHEMA(
    MaxPool,
    10,
    OpSchema()
        .FillUsing(PoolOpSchemaGenerator_10(
            "MaxPool",
            "max",
            "The output of each pooling window is maximum number of elements exclude pad.",
            true,
            10))
        .Attr(
            "storage_order",
            "The storage order of the tensor. 0 is row major, and 1 is column major.",
            AttributeProto::INT,
            static_cast<int64_t>(0))
        .Attr(
            "dilations",
            "Dilation value along each spatial axis of filter.",
            AttributeProto::INTS,
            OPTIONAL_VALUE)
        .Output(
            1,
            "Indices",
            "Indices tensor from max pooling across the input tensor. "
            "The dimensions of indices are the same as output tensor. "
            "The values in indices of are the indices of the selected values during pooling. "
            "The indices are computed as flatten 1-D tensor, "
            "and the indices do not consider padding. "
            "So the values in indices are in [0, N x C x D1 x ... x Dn).",
            "I",
            OpSchema::Optional)
        .TypeConstraint(
            "I",
            {"tensor(int64)"},
            "Constrain index tensor to int64"));

ONNX_OPERATOR_SET_SCHEMA(
    MaxPool,
    11,
    OpSchema()
        .FillUsing(PoolOpSchemaGenerator_10(
            "MaxPool",
            "max",
            "The output of each pooling window is maximum number of elements exclude pad.",
            true,
            11))
        .Attr(
            "storage_order",
            "The storage order of the tensor. 0 is row major, and 1 is column major.",
            AttributeProto::INT,
            static_cast<int64_t>(0))
        .Attr(
            "dilations",
            "Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.",
            AttributeProto::INTS,
            OPTIONAL_VALUE)
        .Output(
            1,
            "Indices",
            "Indices tensor from max pooling across the input tensor. "
            "The dimensions of indices are the same as output tensor. "
            "The values in indices of are the indices of the selected values during pooling. "
            "The indices are computed as flatten 1-D tensor, "
            "and the indices do not consider padding. "
            "So the values in indices are in [0, N x C x D1 x ... x Dn).",
            "I",
            OpSchema::Optional)
        .TypeConstraint(
            "I",
            {"tensor(int64)"},
            "Constrain index tensor to int64"));

void maxUnpoolShapeInference1(InferenceContext& ctx) {
  // we need at least two inputs to have a shape for this inference.
  if (ctx.getNumInputs() != 2 && ctx.getNumInputs() != 3) {
    fail_type_inference("MaxUnpool op must have either two or three inputs.");
  }
  propagateElemTypeFromInputToOutput(ctx, 0, 0);
  if (!hasInputShape(ctx, 0)) {
    return; // If first input does not have shape, we cannot infer much.
  }
  auto input_shape = ctx.getInputType(0)->tensor_type().shape();
  if (input_shape.dim_size() < 2) {
    fail_shape_inference("Input tensor X must have atleast 2 dimensions.");
  }

  // first dim is the batch axis and the next is the number of channels.
  size_t n_input_dims = static_cast<size_t>(input_shape.dim_size() - 2);

  std::vector<int64_t> pads;
  if (getRepeatedAttribute(ctx, "pads", pads)) {
    if (pads.size() != n_input_dims * 2) {
      fail_shape_inference("Attribute pads has incorrect size.");
    }
  } else {
    pads.assign(n_input_dims * 2, 0);
  }

  std::vector<int64_t> strides;
  if (getRepeatedAttribute(ctx, "strides", strides)) {
    if (strides.size() != n_input_dims) {
      fail_shape_inference("Attribute strides has incorrect size.");
    }
  } else {
    strides.assign(n_input_dims, 1);
  }

  std::vector<int64_t> kernel_shape;
  if (getRepeatedAttribute(ctx, "kernel_shape", kernel_shape)) {
    if (kernel_shape.size() != n_input_dims) {
      fail_shape_inference("Attribute kernel_shape has incorrect size.");
    }
  } else {
    fail_shape_inference("Attribute kernel_shape must be specified.");
  }

  if (ctx.getNumInputs() == 3) {
    // If the third input, output_size, is specified, then use that instead
    // of inferring shape from inputs.
    if (hasInputShape(ctx, 2)) {
      auto& output_shape = getInputShape(ctx, 2);
      if (output_shape.dim_size() != 1) {
        fail_type_inference("'output_shape' must be rank 1 tensor.");
      }
      if (output_shape.dim((int)0).has_dim_value() &&
          static_cast<int>(output_shape.dim((int)0).dim_value()) !=
              input_shape.dim_size()) {
        fail_shape_inference(
            "'output_shape' must have same number of elements as the shape of input tensor X.");
      }
    }
    return; // 'output_shape' is specified as input. Actual shape will be
            // determined at runtime.
  }

  auto final_output_shape =
      ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();

  *final_output_shape->add_dim() = input_shape.dim(0);
  *final_output_shape->add_dim() =
      ctx.getInputType(1)->tensor_type().shape().dim(
          1); // channels should be the second dim of second input.

  int kernel_shape_size = static_cast<int>(kernel_shape.size());
  for (int i = 0; i < kernel_shape_size; ++i) {
    auto newdim = final_output_shape->add_dim();
    if (!input_shape.dim(2 + i).has_dim_value()) {
      continue;
    }

    int64_t newdim_value =
        strides[i] * (input_shape.dim(2 + i).dim_value() - 1);
    newdim_value += kernel_shape[i];
    newdim_value -= pads[i];
    newdim_value -= pads[i + kernel_shape_size];

    // add in the initial position
    newdim->set_dim_value(newdim_value);
  }
}

static const char* MaxUnpool_ver9_doc = R"DOC(
MaxUnpool essentially computes the partial inverse of the MaxPool op.
 The input information to this op is typically the the output information from a MaxPool op. The first
 input tensor X is the tensor that needs to be unpooled, which is typically the pooled tensor (first output)
 from MaxPool. The second input tensor, I, contains the indices to the (locally maximal) elements corrsponding
 to the elements in the first input tensor X. Input tensor I is typically the second output of the MaxPool op.
 The third (optional) input is a tensor that specifies the output size of the unpooling operation.

MaxUnpool is intended to do 'partial' inverse of the MaxPool op. 'Partial' because all the non-maximal
 values from the original input to MaxPool are set to zero in the output of the MaxUnpool op. Pooling
 the result of an unpooling operation should give back the original input to the unpooling op.

MaxUnpool can produce the same output size for several input sizes, which makes unpooling op ambiguous.
 The third input argument, output_size, is meant to disambiguate the op and produce output tensor of
 known/predictable size.

In addition to the inputs, MaxUnpool takes three attributes, namely kernel_shape, strides, and pads,
 which define the exact unpooling op. The attributes typically have the same values as the corrsponding
 pooling op that the unpooling op is trying to invert.
)DOC";

ONNX_OPERATOR_SET_SCHEMA(
    MaxUnpool,
    9,
    OpSchema()
        .SetDoc(MaxUnpool_ver9_doc)
        .Attr(
            "kernel_shape",
            "The size of the kernel along each axis.",
            AttributeProto::INTS)
        .Attr(
            "strides",
            "Stride along each spatial axis.",
            AttributeProto::INTS,
            OPTIONAL_VALUE)
        .Attr("pads", pads_doc2, AttributeProto::INTS, OPTIONAL_VALUE)
        .Input(
            0,
            "X",
            "Input data tensor that has to be unpooled. "
            "This tensor is typically the first output of the MaxPool op."
            "Dimensions for image case are (N x C x H x W), "
            "where N is the batch size, C is the number of "
            "channels, and H and W are the height and the "
            "width of the data. For non-image case, the "
            "dimensions are in the form of "
            "(N x C x D1 x D2 ... Dn), where N is the batch "
            "size. Optionally, if dimension denotation is "
            "in effect, the operation expects the input "
            "data tensor to arrive with the dimension denotation "
            "of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].",
            "T1")
        .Input(
            1,
            "I",
            "Input data tensor containing the indices corresponding to "
            "elements in the first input tensor X."
            "This tensor is typically the second output of the MaxPool op."
            "Dimensions must be the same as input tensor X. "
            "The indices are linear, i.e. computed considering the tensor as flattened 1-D tensor, "
            "assuming row-major storage. Also, the linear indices should not consider padding. "
            "So the values in indices are in the range [0, N x C x D1 x ... x Dn).",
            "T2")
        .Input(
            2,
            "output_shape",
            "The shape of the output can be explicitly set which will cause pads values to be auto generated. If 'output_shape' is specified, "
            "'pads' values are ignored.",
            "T2",
            OpSchema::Optional)
        .Output(
            0,
            "output",
            "Output data tensor that contains the result of the unpooling.",
            "T1")
        .TypeConstraint(
            "T1",
            {"tensor(float16)", "tensor(float)", "tensor(double)"},
            "Constrain input and output types to float tensors.")
        .TypeConstraint(
            "T2",
            {"tensor(int64)"},
            "Constrain index tensor to int64")
        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
          maxUnpoolShapeInference1(ctx);
        }));

const char* pads_doc1 =
    "Padding for the beginning and ending along each axis, it can take any value greater "
    "than or equal to 0. The value represent the number of pixels added to the beginning "
    "and end part of the corresponding axis. `pads` format should be as follow "
    "[x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels "
    "added at the beginning of axis `i` and xi_end, the number of pixels added at "
    "the end of axis `i`. This attribute cannot be used simultaneously with "
    "auto_pad attribute.";
const char* auto_pad_doc1 =
    "auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where "
    "default value is NOTSET, which means explicit padding is used. "
    "SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input."
    "In case of odd number add the extra padding at the end for SAME_UPPER and at the "
    "beginning for SAME_LOWER. VALID mean no padding. DEPRECATION NOTE: auto_pad is "
    "only intended to support legacy uses, and for framework authors, one is explicitly "
    "encouraged to use explicit padding specified in the pads attribute.";

static const char* LpPool_ver1_doc = R"DOC(
 LpPool consumes an input tensor X and applies Lp pooling across the
 the tensor according to kernel sizes, stride sizes, and pad lengths.
 Lp pooling consisting of computing the Lp norm on all values of a subset
 of the input tensor according to the kernel size and downsampling the
 data into the output tensor Y for further processing.)DOC";

ONNX_OPERATOR_SET_SCHEMA(
    LpPool,
    1,
    OpSchema()
        .SetDoc(LpPool_ver1_doc)
        .Attr(
            "kernel_shape",
            "The size of the kernel along each axis.",
            AttributeProto::INTS,
            OPTIONAL_VALUE)
        .Attr(
            "strides",
            "Stride along each axis.",
            AttributeProto::INTS,
            OPTIONAL_VALUE)
        .Attr(
            "auto_pad",
            auto_pad_doc1,
            AttributeProto::STRING,
            std::string("NOTSET"))
        .Attr("pads", pads_doc1, AttributeProto::INTS, OPTIONAL_VALUE)
        .Attr(
            "p",
            "p value of the Lp norm used to pool over the input data, default is 2.0.",
            AttributeProto::FLOAT,
            2.0f)
        .Input(
            0,
            "X",
            "Input data tensor from the previous operator; "
            "dimensions for image case are (N x C x H x W), "
            "where N is the batch size, C is the number of "
            "channels, and H and W are the height and the "
            "width of the data. For non image case, the "
            "dimension are in the form of "
            "(N x C x D1 x D2 ... Dn), where N is the "
            "batch size.",
            "T")
        .Output(
            0,
            "Y",
            "Output data tensor from Lp pooling across the input "
            "tensor. Dimensions will vary based on various kernel, stride, and pad "
            "sizes.",
            "T")
        .TypeConstraint(
            "T",
            {"tensor(float16)", "tensor(float)", "tensor(double)"},
            "Constrain input and output types to float tensors."));

std::function<void(OpSchema&)> LpPoolOpSchemaGenerator_10(const char* name) {
  return [=](OpSchema& schema) {
    std::string doc;
    POPULATE_OP_DOC_STR(doc = R"DOC(
 {name} consumes an input tensor X and applies Lp pooling across
 the tensor according to kernel sizes, stride sizes, and pad lengths.
 Lp pooling consisting of computing the Lp norm on all values of a subset
 of the input tensor according to the kernel size and downsampling the
 data into the output tensor Y for further processing.)DOC";
                        ReplaceAll(doc, "{name}", name););
    schema.SetDoc(doc);
    schema.Attr(
        "kernel_shape",
        "The size of the kernel along each axis.",
        AttributeProto::INTS);
    schema.Attr(
        "strides",
        "Stride along each spatial axis.",
        AttributeProto::INTS,
        OPTIONAL_VALUE);
    schema.Attr(
        "auto_pad",
        auto_pad_doc2,
        AttributeProto::STRING,
        std::string("NOTSET"));
    schema.Attr("pads", pads_doc2, AttributeProto::INTS, OPTIONAL_VALUE);
    schema.Attr(
        "p",
        "p value of the Lp norm used to pool over the input data.",
        AttributeProto::INT,
        static_cast<int64_t>(2));
    schema.Input(
        0,
        "X",
        "Input data tensor from the previous operator; "
        "dimensions for image case are (N x C x H x W), "
        "where N is the batch size, C is the number of "
        "channels, and H and W are the height and the "
        "width of the data. For non image case, the "
        "dimensions are in the form of "
        "(N x C x D1 x D2 ... Dn), where N is the "
        "batch size.",
        "T");
    schema.Output(
        0,
        "Y",
        "Output data tensor from Lp pooling across the input "
        "tensor. Dimensions will vary based on various kernel, stride, and pad "
        "sizes.",
        "T");
    schema.TypeConstraint(
        "T",
        {"tensor(float16)", "tensor(float)", "tensor(double)"},
        "Constrain input and output types to float tensors.");
    schema.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
      propagateElemTypeFromInputToOutput(ctx, 0, 0);
      convPoolShapeInference1(ctx, false, true, 0, 1);
    });
  };
}

ONNX_OPERATOR_SET_SCHEMA(
    LpPool,
    2,
    OpSchema().FillUsing(LpPoolOpSchemaGenerator_10("LpPool")));

static const char* GlobalLpPool_ver1_doc = R"DOC(
 GlobalLpPool consumes an input tensor X and applies lp pool pooling across the
 the values in the same channel. This is equivalent to LpPool with kernel size
 equal to the spatial dimension of input tensor.)DOC";

std::function<void(OpSchema&)> ConvOpSchemaGenerator_10(
    const char* filter_desc) {
  return [=](OpSchema& schema) {
    std::string doc;
    POPULATE_OP_DOC_STR(doc = R"DOC(
The convolution operator consumes an input tensor and {filter_desc}, and
computes the output.)DOC";
                        ReplaceAll(doc, "{filter_desc}", filter_desc););
    schema.SetDoc(doc);
    schema.Input(
        0,
        "X",
        "Input data tensor from previous layer; "
        "has size (N x C x H x W), where N is the batch size, "
        "C is the number of channels, and H and W are the "
        "height and width. Note that this is for the 2D image. "
        "Otherwise the size is (N x C x D1 x D2 ... x Dn). "
        "Optionally, if dimension denotation is "
        "in effect, the operation expects input data tensor "
        "to arrive with the dimension denotation of [DATA_BATCH, "
        "DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].",
        "T");
    schema.Input(
        1,
        "W",
        "The weight tensor that will be used in the "
        "convolutions; has size (M x C/group x kH x kW), where C "
        "is the number of channels, and kH and kW are the "
        "height and width of the kernel, and M is the number "
        "of feature maps. For more than 2 dimensions, the "
        "kernel shape will be (M x C/group x k1 x k2 x ... x kn), "
        "where (k1 x k2 x ... kn) is the dimension of the kernel. "
        "Optionally, if dimension denotation is in effect, "
        "the operation expects the weight tensor to arrive "
        "with the dimension denotation of [FILTER_OUT_CHANNEL, "
        "FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. "
        "X.shape[1] == (W.shape[1] * group) == C "
        "(assuming zero based indices for the shape array). "
        "Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL. ",
        "T");
    schema.Input(
        2,
        "B",
        "Optional 1D bias to be added to the convolution, has size of M.",
        "T",
        OpSchema::Optional);
    schema.Output(
        0,
        "Y",
        "Output data tensor that contains the result of the "
        "convolution. The output dimensions are functions "
        "of the kernel size, stride size, and pad lengths.",
        "T");
    schema.TypeConstraint(
        "T",
        {"tensor(float16)", "tensor(float)", "tensor(double)"},
        "Constrain input and output types to float tensors.");
    schema.Attr(
        "kernel_shape",
        "The shape of the convolution kernel. If not present, should be inferred from input W.",
        AttributeProto::INTS,
        OPTIONAL_VALUE);
    schema.Attr(
        "dilations",
        "dilation value along each spatial axis of the filter.",
        AttributeProto::INTS,
        OPTIONAL_VALUE);
    schema.Attr(
        "strides",
        "Stride along each spatial axis.",
        AttributeProto::INTS,
        OPTIONAL_VALUE);
    schema.Attr(
        "auto_pad",
        auto_pad_doc2,
        AttributeProto::STRING,
        std::string("NOTSET"));
    schema.Attr("pads", pads_doc2, AttributeProto::INTS, OPTIONAL_VALUE);
    schema.Attr(
        "group",
        "number of groups input channels and output channels are divided into.",
        AttributeProto::INT,
        static_cast<int64_t>(1));
    schema.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
      propagateElemTypeFromInputToOutput(ctx, 0, 0);
      convPoolShapeInference1(ctx, true, false, 0, 1);
    });
  };
}

ONNX_OPERATOR_SET_SCHEMA(
    Conv,
    1,
    OpSchema().FillUsing(ConvOpSchemaGenerator_10("a filter")));

void convTransposeShapeInference1(InferenceContext& ctx) {
  propagateElemTypeFromInputToOutput(ctx, 0, 0);

  // we need at least two inputs to have a shape for this inference.
  if (!hasNInputShapes(ctx, 2)) {
    return;
  }

  int64_t group = getAttribute(ctx, "group", 1);

  auto input_shape = ctx.getInputType(0)->tensor_type().shape();
  if (input_shape.dim_size() < 2) {
    return; // Input tensor should have at least two dimensions.
  }

  // first dim is the batch axis and the next is the number of channels.
  size_t n_input_dims = static_cast<size_t>(input_shape.dim_size() - 2);

  std::vector<int64_t> dilations;
  if (getRepeatedAttribute(ctx, "dilations", dilations)) {
    if (dilations.size() != n_input_dims) {
      return;
    }
  } else {
    dilations.assign(n_input_dims, 1);
  }

  std::vector<int64_t> strides;
  if (getRepeatedAttribute(ctx, "strides", strides)) {
    if (strides.size() != n_input_dims) {
      return;
    }
  } else {
    strides.assign(n_input_dims, 1);
  }

  std::vector<int64_t> kernel_shape;
  if (getRepeatedAttribute(ctx, "kernel_shape", kernel_shape)) {
    if (kernel_shape.size() != n_input_dims) {
      return;
    }
  } else {
    auto second_input_shape = ctx.getInputType(1)->tensor_type().shape();
    for (int i = 2; i < second_input_shape.dim_size(); ++i) {
      if (!second_input_shape.dim(i).has_dim_value()) {
        return;
      }
      kernel_shape.push_back(second_input_shape.dim(i).dim_value());
    }
  }

  std::vector<int64_t> effective_kernel_shape = kernel_shape;
  for (int i = 0; i < static_cast<int>(kernel_shape.size()); i++) {
    // accounting for dilation, how big is the kernel in this dimension
    effective_kernel_shape[i] =
        (effective_kernel_shape[i] - 1) * dilations[i] + 1;
  }

  std::vector<int64_t> pads;
  if (getRepeatedAttribute(ctx, "pads", pads)) {
    if (pads.size() != n_input_dims * 2) {
      fail_shape_inference("Attribute pads has incorrect size");
    }
  } else {
    pads.assign(n_input_dims * 2, 0);
    const auto* auto_pad_attr = ctx.getAttribute("auto_pad");
    if ((nullptr != auto_pad_attr) && (auto_pad_attr->s() != "VALID")) {
      int input_dims_size = static_cast<int>(n_input_dims);
      for (int i = 0; i < input_dims_size; ++i) {
        int64_t residual = 0;
        int64_t stride = strides[i];
        if (stride > 1) {
          if (!input_shape.dim(2 + i).has_dim_value()) {
            continue;
          }
          residual = input_shape.dim(2 + i).dim_value();
          while (residual >= stride) {
            residual -= stride;
          }
        }
        int64_t total_pad = residual == 0
            ? effective_kernel_shape[i] - stride
            : effective_kernel_shape[i] - residual;
        if (total_pad < 0)
          total_pad = 0;
        int64_t half_pad_small = total_pad >> 1;
        int64_t half_pad_big = total_pad - half_pad_small;
        if (auto_pad_attr->s() == "SAME_UPPER") {
          pads[i] = half_pad_small;
          pads[i + input_dims_size] = half_pad_big;
        } else if (auto_pad_attr->s() == "SAME_LOWER") {
          pads[i] = half_pad_big;
          pads[i + input_dims_size] = half_pad_small;
        }
      }
    }
  }

  std::vector<int64_t> output_shape;
  bool output_shape_presented = true;
  if (getRepeatedAttribute(ctx, "output_shape", output_shape)) {
    if (output_shape.size() != n_input_dims) {
      return;
    }
  } else {
    output_shape_presented = false;
  }

  std::vector<int64_t> output_padding;
  if (getRepeatedAttribute(ctx, "output_padding", output_padding)) {
    if (output_padding.size() != n_input_dims) { // Added only to one side.
      return;
    }
  } else {
    output_padding.assign(n_input_dims, 0);
  }

  auto final_output_shape =
      ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();

  *final_output_shape->add_dim() = input_shape.dim(0);
  *final_output_shape->add_dim() =
      ctx.getInputType(1)->tensor_type().shape().dim(1) *
      group; // channels should be the second dim of second input multiply
             // group.

  int size_of_output;
  if (output_shape_presented) {
    size_of_output = static_cast<int>(output_shape.size());
    for (int i = 0; i < size_of_output; ++i) {
      if (input_shape.dim(i + 2).has_dim_value()) {
        if (output_shape[i] < input_shape.dim(i + 2).dim_value()) {
          // TODO: throw exception?
          return; // output shape value cannot be smaller than the input shape
                  // value
        }
      }
      final_output_shape->add_dim()->set_dim_value(output_shape[i]);
    }
    return;
  } else {
    size_of_output = input_shape.dim_size() - 2;
    for (int i = 0; i < size_of_output; ++i) {
      if (input_shape.dim(i + 2).has_dim_value()) {
        int64_t output_shape_dim =
            strides[i] * (input_shape.dim(i + 2).dim_value() - 1) +
            output_padding[i] + effective_kernel_shape[i] - pads[i] -
            pads[i + n_input_dims];
        final_output_shape->add_dim()->set_dim_value(output_shape_dim);
      } else {
        final_output_shape->add_dim();
      }
    }
    return;
  }
}

std::function<void(OpSchema&)> ConvTransposeOpSchemaGenerator_10(
    const char* filter_desc) {
  return [=](OpSchema& schema) {
    std::string doc;
    POPULATE_OP_DOC_STR(doc = R"DOC(
The convolution transpose operator consumes an input tensor and {filter_desc},
and computes the output.

If the pads parameter is provided the shape of the output is calculated via the following equation:

  output_shape[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - pads[start_i] - pads[end_i]

output_shape can also be explicitly specified in which case pads values are auto generated using these equations:

  total_padding[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - output_shape[i]
  If (auto_pads != SAME_UPPER): pads[start_i] = total_padding[i]/2; pads[end_i] = total_padding[i] - (total_padding[i]/2)
  Else: pads[start_i] = total_padding[i] - (total_padding[i]/2); pads[end_i] = (total_padding[i]/2).

    )DOC";
                        ReplaceAll(doc, "{filter_desc}", filter_desc););
    schema.SetDoc(doc);
    schema.Input(
        0,
        "X",
        "Input data tensor from previous layer; has size (N x C x H x W)"
        ", where N is the batch size, C is the number of channels, and"
        " H and W are the height and width. Note that this is for the 2D image. "
        "Otherwise the size is (N x C x D1 x D2 ... x Dn)",
        "T");
    schema.Input(
        1,
        "W",
        "The weight tensor that will be used in the "
        "convolutions; has size (C x M/group x kH x kW), where C "
        "is the number of channels, and kH and kW are the "
        "height and width of the kernel, and M is the number "
        "of feature maps. For more than 2 dimensions, the "
        "weight shape will be (C x M/group x k1 x k2 x ... x kn), "
        "where (k1 x k2 x ... x kn) is the dimension of the kernel. "
        "The number of channels in the output should be equal to W.shape[1] * group "
        "(assuming zero based indices of the shape array)",
        "T");
    schema.Input(
        2,
        "B",
        "Optional 1D bias to be added to the convolution, has size of M.",
        "T",
        OpSchema::Optional);
    schema.Output(
        0,
        "Y",
        "Output data tensor that contains the result of the convolution. The "
        "output dimensions are functions of the kernel size, stride size, "
        "pad lengths and group count. "
        "The number of channels in the output should be equal to W.shape[1] * group "
        "(assuming zero based indices of the shape array)",
        "T");
    schema.TypeConstraint(
        "T",
        {"tensor(float16)", "tensor(float)", "tensor(double)"},
        "Constrain input and output types to float tensors.");
    schema.Attr(
        "kernel_shape",
        "The shape of the convolution kernel. If not present, should be inferred from input W.",
        AttributeProto::INTS,
        OPTIONAL_VALUE);
    schema.Attr(
        "output_shape",
        "The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified "
        "pads values are ignored. See doc for details for equations to generate pads",
        AttributeProto::INTS,
        OPTIONAL_VALUE);
    schema.Attr(
        "output_padding",
        "The zero-padding added to one side of the output."
        " This is also called adjs/adjustment in some frameworks.",
        AttributeProto::INTS,
        OPTIONAL_VALUE);
    schema.Attr(
        "dilations",
        "dilation value along each spatial axis of the filter.",
        AttributeProto::INTS,
        OPTIONAL_VALUE);
    schema.Attr(
        "strides",
        "Stride along each spatial axis.",
        AttributeProto::INTS,
        OPTIONAL_VALUE);
    schema.Attr(
        "auto_pad",
        auto_pad_doc2,
        AttributeProto::STRING,
        std::string("NOTSET"));
    schema.Attr("pads", pads_doc2, AttributeProto::INTS, OPTIONAL_VALUE);
    schema.Attr(
        "group",
        "number of groups input channels and output channels are divided into.",
        AttributeProto::INT,
        static_cast<int64_t>(1));
    schema.TypeAndShapeInferenceFunction(
        [](InferenceContext& ctx) { convTransposeShapeInference1(ctx); });
  };
}

ONNX_OPERATOR_SET_SCHEMA(
    ConvTranspose,
    1,
    OpSchema().FillUsing(ConvTransposeOpSchemaGenerator_10("a filter")));

ONNX_OPERATOR_SET_SCHEMA(
    GlobalLpPool,
    1,
    OpSchema()
        .SetDoc(GlobalLpPool_ver1_doc)
        .Attr(
            "p",
            "p value of the Lp norm used to pool over the input data, default is 2.0.",
            AttributeProto::FLOAT,
            2.0f)
        .Input(
            0,
            "X",
            "Input data tensor from the previous operator; "
            "dimensions for image case are (N x C x H x W), "
            "where N is the batch size, C is the number of "
            "channels, and H and W are the height and the width "
            "of the data. For non image case, the dimension are "
            "in the form of (N x C x D1 x D2 ... Dn), "
            "where N is the batch size.",
            "T")
        .Output(
            0,
            "Y",
            "Output data tensor from pooling across the input "
            "tensor. Dimensions will be N x C x 1 x 1",
            "T")
        .TypeConstraint(
            "T",
            {"tensor(float16)", "tensor(float)", "tensor(double)"},
            "Constrain input and output types to float tensors."));

static const char* BatchNormalization_ver1_doc = R"DOC(
Carries out batch normalization as described in the paper
https://arxiv.org/abs/1502.03167. Depending on the mode it is being run,
there are multiple cases for the number of outputs, which we list below:

Output case #1: Y, mean, var, saved_mean, saved_var (training mode)
Output case #2: Y (test mode)
    )DOC";

ONNX_OPERATOR_SET_SCHEMA(
    BatchNormalization,
    1,
    OpSchema()
        .NumOutputs({1, 5})
        .SetDoc(BatchNormalization_ver1_doc)
        .Attr(
            "spatial",
            "If true, compute the mean and variance across all spatial elements "
            "If false, compute the mean and variance across per feature."
            "Default is 1.",
            AttributeProto::INT,
            static_cast<int64_t>(1))
        .Attr(
            "is_test",
            "If set to nonzero, run spatial batch normalization in test mode, default is 0.",
            AttributeProto::INT,
            static_cast<int64_t>(0))
        .Attr(
            "epsilon",
            "The epsilon value to use to avoid division by zero, default is 1e-5f.",
            AttributeProto::FLOAT,
            1e-5f)
        .Attr(
            "momentum",
            "Factor used in computing the running mean and variance."
            "e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.",
            AttributeProto::FLOAT,
            0.9f)
        // This attribute was added via AllowConsumed API in OpSchema.
        // After removing the API, we're now using the Attr API to simulate the
        // old definition.
        .Attr(
            "consumed_inputs",
            "legacy optimization attribute.",
            AttributeProto::INTS)
        .Input(0, "X", "The input 4-dimensional tensor of shape NCHW.", "T")
        .Input(
            1,
            "scale",
            "The scale as a 1-dimensional tensor of size C to be applied to the "
            "output.",
            "T")
        .Input(
            2,
            "B",
            "The bias as a 1-dimensional tensor of size C to be applied to the "
            "output.",
            "T")
        .Input(
            3,
            "mean",
            "The running mean (training) or the estimated mean (testing) "
            "as a 1-dimensional tensor of size C.",
            "T")
        .Input(
            4,
            "var",
            "The running variance (training) or the estimated "
            "variance (testing) as a 1-dimensional tensor of size C.",
            "T")
        .Output(
            0,
            "Y",
            "The output 4-dimensional tensor of the same shape as X.",
            "T")
        .Output(
            1,
            "mean",
            "The running mean after the BatchNormalization operator. Must be in-place "
            "with the input mean. Should not be used for testing.",
            "T",
            OpSchema::Optional)
        .Output(
            2,
            "var",
            "The running variance after the BatchNormalization operator. Must be "
            "in-place with the input var. Should not be used for testing.",
            "T",
            OpSchema::Optional)
        .Output(
            3,
            "saved_mean",
            "Saved mean used during training to speed up gradient "
            "computation. Should not be used for testing.",
            "T",
            OpSchema::Optional)
        .Output(
            4,
            "saved_var",
            "Saved variance used during training to speed up "
            "gradient computation. Should not be used for testing.",
            "T",
            OpSchema::Optional)
        .TypeConstraint(
            "T",
            {"tensor(float16)", "tensor(float)", "tensor(double)"},
            "Constrain input and output types to float tensors."));

static const char* BatchNormalization_ver9_doc = R"DOC(
Carries out batch normalization as described in the paper
https://arxiv.org/abs/1502.03167. Depending on the mode it is being run,
there are multiple cases for the number of outputs, which we list below:

Output case #1: Y, mean, var, saved_mean, saved_var (training mode)
Output case #2: Y (test mode)

For previous (depreciated) non-spatial cases, implementors are suggested
to flatten the input shape to (N x C*D1*D2 ..*Dn) before a BatchNormalization Op.
)DOC";

ONNX_OPERATOR_SET_SCHEMA(
    BatchNormalization,
    9,
    OpSchema()
        .NumOutputs({1, 5})
        .SetDoc(BatchNormalization_ver9_doc + GenerateOptionalArgumentsDoc())
        .Attr(
            "epsilon",
            "The epsilon value to use to avoid division by zero.",
            AttributeProto::FLOAT,
            1e-5f)
        .Attr(
            "momentum",
            "Factor used in computing the running mean and variance."
            "e.g., running_mean = running_mean * momentum + mean * (1 - momentum).",
            AttributeProto::FLOAT,
            0.9f)
        .Input(
            0,
            "X",
            "Input data tensor from the previous operator; "
            "dimensions are in the form of (N x C x D1 x D2 ... Dn), "
            "where N is the batch size, C is the number of channels. "
            "Statistics are computed for every channel of C over N and D1 to Dn dimensions. "
            "For image data, input dimensions become (N x C x H x W). "
            "The op also accepts single dimension input of size N in which case C is assumed to be 1",
            "T",
            OpSchema::Single,
            true,
            1,
            OpSchema::Differentiable)
        .Input(
            1,
            "scale",
            "Scale tensor of shape (C).",
            "T",
            OpSchema::Single,
            true,
            1,
            OpSchema::Differentiable)
        .Input(
            2,
            "B",
            "Bias tensor of shape (C).",
            "T",
            OpSchema::Single,
            true,
            1,
            OpSchema::Differentiable)
        .Input(
            3,
            "mean",
            "running (training) or estimated (testing) mean tensor of shape (C).",
            "T",
            OpSchema::Single,
            true,
            1,
            OpSchema::Differentiable)
        .Input(
            4,
            "var",
            "running (training) or estimated (testing) variance tensor of shape (C).",
            "T",
            OpSchema::Single,
            true,
            1,
            OpSchema::Differentiable)
        .Output(
            0,
            "Y",
            "The output tensor of the same shape as X",
            "T",
            OpSchema::Single,
            true,
            1,
            OpSchema::Differentiable)
        .Output(
            1,
            "mean",
            "The running mean after the BatchNormalization operator.",
            "T",
            OpSchema::Optional,
            true,
            1,
            OpSchema::NonDifferentiable)
        .Output(
            2,
            "var",
            "The running variance after the BatchNormalization operator.",
            "T",
            OpSchema::Optional,
            true,
            1,
            OpSchema::NonDifferentiable)
        .Output(
            3,
            "saved_mean",
            "Saved mean used during training to speed up gradient "
            "computation.",
            "T",
            OpSchema::Optional,
            true,
            1,
            OpSchema::NonDifferentiable)
        .Output(
            4,
            "saved_var",
            "Saved variance used during training to speed up "
            "gradient computation.",
            "T",
            OpSchema::Optional,
            true,
            1,
            OpSchema::NonDifferentiable)
        .TypeConstraint(
            "T",
            {"tensor(float16)", "tensor(float)", "tensor(double)"},
            "Constrain input and output types to float tensors.")
        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
          propagateShapeAndTypeFromFirstInput(ctx);
          // TODO in training mode, it may be possible to infer some of
          // the other outputs as well.
        }));

static const char* BatchNormalization_ver14_doc = R"DOC(
Carries out batch normalization as described in the paper
https://arxiv.org/abs/1502.03167. Depending on the mode it is being run,
There are five required inputs 'X', 'scale', 'B', 'input_mean' and
'input_var'.
Note that 'input_mean' and 'input_var' are expected to be the estimated
statistics in inference mode (training_mode=False, default),
and the running statistics in training mode (training_mode=True).
There are multiple cases for the number of outputs, which we list below:

Output case #1: Y, running_mean, running_var (training_mode=True)
Output case #2: Y (training_mode=False)

When training_mode=False, extra outputs are invalid.
The outputs are updated as follows when training_mode=True:
```
running_mean = input_mean * momentum + current_mean * (1 - momentum)
running_var = input_var * momentum + current_var * (1 - momentum)

Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B

where:

current_mean = ReduceMean(X, axis=all_except_channel_index)
current_var =  ReduceVar(X, axis=all_except_channel_index)

Notice that ReduceVar refers to the population variance, and it equals to
sum(sqrd(x_i - x_avg)) / N
where N is the population size (this formula does not use sample size N - 1).

```

When training_mode=False:
```
Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B
```

For previous (depreciated) non-spatial cases, implementors are suggested
to flatten the input shape to (N x C * D1 * D2 * ... * Dn) before a BatchNormalization Op.
)DOC";

ONNX_OPERATOR_SET_SCHEMA(
    BatchNormalization,
    14,
    OpSchema()
        .NumOutputs({1, 3})
        .SetDoc(BatchNormalization_ver14_doc + GenerateOptionalArgumentsDoc())
        .Attr(
            "epsilon",
            "The epsilon value to use to avoid division by zero.",
            AttributeProto::FLOAT,
            1e-5f)
        .Attr(
            "momentum",
            "Factor used in computing the running mean and variance."
            "e.g., running_mean = running_mean * momentum + mean * (1 - momentum).",
            AttributeProto::FLOAT,
            0.9f)
        .Attr(
            "training_mode",
            "If set to true, it indicates BatchNormalization is being used for training, and outputs 1, "
            "2, 3, and 4 would be populated.",
            AttributeProto::INT,
            static_cast<int64_t>(0))
        .Input(
            0,
            "X",
            "Input data tensor from the previous operator; "
            "dimensions are in the form of (N x C x D1 x D2 ... Dn), "
            "where N is the batch size, C is the number of channels. "
            "Statistics are computed for every channel of C over N and D1 to Dn dimensions. "
            "For image data, input dimensions become (N x C x H x W). "
            "The op also accepts single dimension input of size N in which case C is assumed to be 1",
            "T",
            OpSchema::Single,
            true,
            1,
            OpSchema::Differentiable)
        .Input(
            1,
            "scale",
            "Scale tensor of shape (C).",
            "T",
            OpSchema::Single,
            true,
            1,
            OpSchema::Differentiable)
        .Input(
            2,
            "B",
            "Bias tensor of shape (C).",
            "T",
            OpSchema::Single,
            true,
            1,
            OpSchema::Differentiable)
        .Input(
            3,
            "input_mean",
            "running (training) or estimated (testing) mean tensor of shape (C).",
            "U",
            OpSchema::Single,
            true,
            1,
            OpSchema::Differentiable)
        .Input(
            4,
            "input_var",
            "running (training) or estimated (testing) variance tensor of shape (C).",
            "U",
            OpSchema::Single,
            true,
            1,
            OpSchema::Differentiable)
        .Output(
            0,
            "Y",
            "The output tensor of the same shape as X",
            "T",
            OpSchema::Single,
            true,
            1,
            OpSchema::Differentiable)
        .Output(
            1,
            "running_mean",
            "The running mean after the BatchNormalization operator.",
            "U",
            OpSchema::Optional,
            true,
            1,
            OpSchema::NonDifferentiable)
        .Output(
            2,
            "running_var",
            "The running variance after the BatchNormalization operator. This op uses the population size (N) for "
            "calculating variance, and not the sample size N-1.",
            "U",
            OpSchema::Optional,
            true,
            1,
            OpSchema::NonDifferentiable)
        .TypeConstraint(
            "T",
            {"tensor(float16)", "tensor(float)", "tensor(double)", "tensor(bfloat16)"},
            "Constrain input and output types to float tensors.")
        .TypeConstraint(
            "U",
            {"tensor(float16)", "tensor(float)", "tensor(double)", "tensor(bfloat16)"},
            "Constrain mean and variance types to float tensors. It allows all float type for U.")
        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
          propagateShapeAndTypeFromFirstInput(ctx);
          propagateShapeFromInputToOutput(ctx, 0, 0);

          Dim num_channels;

          unifyInputDim(ctx, 0, 1, num_channels);
          unifyInputDim(ctx, 1, 0, num_channels);
          unifyInputDim(ctx, 2, 0, num_channels);
          unifyInputDim(ctx, 3, 0, num_channels);
          unifyInputDim(ctx, 4, 0, num_channels);

          if (ctx.getAttribute("training_mode") &&
               static_cast<int>(ctx.getAttribute("training_mode")->i()) != 0) {
            if (ctx.getNumOutputs() != 3)
              fail_shape_inference(
                "This number of op outputs should be 3 when Training_mode = True, but it is not.");
          } else {
            if (ctx.getNumOutputs() != 1)
              fail_shape_inference(
                "This number of op outputs should be 1 when Training_mode = False, but it is not.");
          }

          if (ctx.getNumOutputs() > 1) {
            TensorShapeProto outputs_shape;
            *outputs_shape.add_dim() = num_channels; // channel

            propagateElemTypeFromInputToOutput(ctx, 3, 1);
            updateOutputShape(ctx, 1, outputs_shape);

            if (ctx.getNumOutputs() > 2) {
              propagateElemTypeFromInputToOutput(ctx, 4, 2);
              updateOutputShape(ctx, 2, outputs_shape);
            }
          }
        }));

static const char* InstanceNormalization_ver1_doc = R"DOC(
Carries out instance normalization as described in the paper
https://arxiv.org/abs/1607.08022.

y = scale * (x - mean) / sqrt(variance + epsilon) + B,
where mean and variance are computed per instance per channel.

)DOC";

ONNX_OPERATOR_SET_SCHEMA(
    InstanceNormalization,
    1,
    OpSchema()
        .SetDoc(InstanceNormalization_ver1_doc)
        // This attribute was added via AllowConsumed API in OpSchema.
        // After removing the API, we're now using the Attr API to simulate the
        // old definition.
        .Attr(
            "consumed_inputs",
            "legacy optimization attribute.",
            AttributeProto::INTS,
            OPTIONAL_VALUE)
        .Attr(
            "epsilon",
            "The epsilon value to use to avoid division by zero, default is 1e-5f.",
            AttributeProto::FLOAT,
            1e-5f)
        .Input(0, "input", "The input 4-dimensional tensor of shape NCHW.", "T")
        .Input(
            1,
            "scale",
            "The input 1-dimensional scale tensor of size C.",
            "T")
        .Input(2, "B", "The input 1-dimensional bias tensor of size C.", "T")
        .Output(
            0,
            "output",
            "The output 4-dimensional tensor of the same shape as input.",
            "T")
        .TypeConstraint(
            "T",
            {"tensor(float16)", "tensor(float)", "tensor(double)"},
            "Constrain input and output types to float tensors."));

static const char* Dropout_old_doc = R"DOC(
Dropout takes one input data (Tensor<float>) and produces two Tensor outputs,
output (Tensor<float>) and mask (Tensor<bool>). Depending on whether it is in
test mode or not, the output Y will either be a random dropout, or a simple
copy of the input. Note that our implementation of Dropout does scaling in
the training phase, so during testing nothing needs to be done.
)DOC";

ONNX_OPERATOR_SET_SCHEMA(
    Dropout,
    1,
    OpSchema()
        .SetDoc(Dropout_old_doc)
        .Attr(
            "ratio",
            "(float, default 0.5) the ratio of random dropout",
            AttributeProto::FLOAT,
            0.5f)
        // This attribute was added via AllowConsumed API in OpSchema.
        // After removing the API, we're now using the Attr API to simulate the
        // old definition.
        .Attr(
            "consumed_inputs",
            "legacy optimization attribute.",
            AttributeProto::INTS,
            OPTIONAL_VALUE)
        .Attr(
            "is_test",
            "(int, default 0) if nonzero, run dropout in test mode where "
            "the output is simply Y = X.",
            AttributeProto::INT,
            static_cast<int64_t>(0))
        .Input(0, "data", "The input data as Tensor.", "T")
        .Output(0, "output", "The output.", "T")
        .Output(
            1,
            "mask",
            "The output mask. If is_test is nonzero, this output is not filled.",
            "T",
            OpSchema::Optional)
        .TypeConstraint(
            "T",
            {"tensor(float16)", "tensor(float)", "tensor(double)"},
            "Constrain input and output types to float tensors."));

ONNX_OPERATOR_SET_SCHEMA(
    Dropout,
    6,
    OpSchema()
        .SetDoc(Dropout_old_doc)
        .Attr(
            "ratio",
            "(float, default 0.5) the ratio of random dropout",
            AttributeProto::FLOAT,
            0.5f)
        .Attr(
            "is_test",
            "(int, default 0) if nonzero, run dropout in test mode where "
            "the output is simply Y = X.",
            AttributeProto::INT,
            static_cast<int64_t>(0))
        .Input(0, "data", "The input data as Tensor.", "T")
        .Output(0, "output", "The output.", "T")
        .Output(
            1,
            "mask",
            "The output mask. If is_test is nonzero, this output is not filled.",
            "T",
            OpSchema::Optional)
        .TypeConstraint(
            "T",
            {"tensor(float16)", "tensor(float)", "tensor(double)"},
            "Constrain input and output types to float tensors.")
        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));

static const char* Dropout_ver7_doc = R"DOC(
Dropout takes one input data (Tensor<float>) and produces two Tensor outputs,
output (Tensor<float>) and mask (Tensor<bool>). Depending on whether it is in
test mode or not, the output Y will either be a random dropout, or a simple
copy of the input. Note that our implementation of Dropout does scaling in
the training phase, so during testing nothing needs to be done.
)DOC";

ONNX_OPERATOR_SET_SCHEMA(
    Dropout,
    7,
    OpSchema()
        .SetDoc(GET_OP_DOC_STR(
            std::string(Dropout_ver7_doc) + GenerateOptionalArgumentsDoc()))
        .Attr(
            "ratio",
            "The ratio of random dropout",
            AttributeProto::FLOAT,
            0.5f)
        .Input(0, "data", "The input data as Tensor.", "T")
        .Output(0, "output", "The output.", "T")
        .Output(1, "mask", "The output mask.", "T", OpSchema::Optional)
        .TypeConstraint(
            "T",
            {"tensor(float16)", "tensor(float)", "tensor(double)"},
            "Constrain input and output types to float tensors.")
        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));

static const char* Dropout_ver10_doc = R"DOC(
Dropout takes one input floating tensor and produces two tensor outputs,
output (floating tensor) and mask (`Tensor<bool>`). Depending on whether it is
in test mode or not, the output Y will either be a random dropout, or a simple
copy of the input. Note that our implementation of Dropout does scaling in
the training phase, so during testing nothing needs to be done.
)DOC";

ONNX_OPERATOR_SET_SCHEMA(
    Dropout,
    10,
    OpSchema()
        .SetDoc(GET_OP_DOC_STR(
            std::string(Dropout_ver10_doc) + GenerateOptionalArgumentsDoc()))
        .Attr(
            "ratio",
            "The ratio of random dropout",
            AttributeProto::FLOAT,
            0.5f)
        .Input(0, "data", "The input data as Tensor.", "T")
        .Output(0, "output", "The output.", "T")
        .Output(1, "mask", "The output mask.", "T1", OpSchema::Optional)
        .TypeConstraint(
            "T",
            {"tensor(float16)", "tensor(float)", "tensor(double)"},
            "Constrain input and output types to float tensors.")
        .TypeConstraint(
            "T1",
            {"tensor(bool)"},
            "Constrain output mask types to boolean tensors.")
        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
          propagateShapeAndTypeFromFirstInput(ctx);
          if (ctx.getNumOutputs() == 2) {
            updateOutputElemType(ctx, 1, TensorProto::BOOL);
            if (hasNInputShapes(ctx, 1)) {
              propagateShapeFromInputToOutput(ctx, 0, 1);
            }
          }
        }));

static const char* BatchNorm_ver6_doc = R"DOC(
Carries out batch normalization as described in the paper
https://arxiv.org/abs/1502.03167. Depending on the mode it is being run,
there are multiple cases for the number of outputs, which we list below:

Output case #1: Y, mean, var, saved_mean, saved_var (training mode)
Output case #2: Y (test mode)
)DOC";

ONNX_OPERATOR_SET_SCHEMA(
    BatchNormalization,
    6,
    OpSchema()
        .NumOutputs({1, 5})
        .SetDoc(BatchNorm_ver6_doc)
        .Attr(
            "spatial",
            "If true, compute the mean and variance across all spatial elements "
            "If false, compute the mean and variance across per feature."
            "Default is 1.",
            AttributeProto::INT,
            static_cast<int64_t>(1))
        .Attr(
            "is_test",
            "If set to nonzero, run spatial batch normalization in test mode, default is 0.",
            AttributeProto::INT,
            static_cast<int64_t>(0))
        .Attr(
            "epsilon",
            "The epsilon value to use to avoid division by zero, default is 1e-5f.",
            AttributeProto::FLOAT,
            1e-5f)
        .Attr(
            "momentum",
            "Factor used in computing the running mean and variance."
            "e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.",
            AttributeProto::FLOAT,
            0.9f)
        .Input(
            0,
            "X",
            "Input data tensor from the previous operator; "
            "dimensions for image case are (N x C x H x W), "
            "where N is the batch size, C is the number of "
            "channels, and H and W are the height and the "
            "width of the data. For non image case, the "
            "dimensions are in the form of "
            "(N x C x D1 x D2 ... Dn), where N is the batch "
            "size.",
            "T")
        .Input(
            1,
            "scale",
            "The scale as a 1-dimensional tensor of size C to be applied to the "
            "output.",
            "T")
        .Input(
            2,
            "B",
            "The bias as a 1-dimensional tensor of size C to be applied to the "
            "output.",
            "T")
        .Input(
            3,
            "mean",
            "The running mean (training) or the estimated mean (testing) "
            "as a 1-dimensional tensor of size C.",
            "T")
        .Input(
            4,
            "var",
            "The running variance (training) or the estimated "
            "variance (testing) as a 1-dimensional tensor of size C.",
            "T")
        .Output(0, "Y", "The output tensor of the same shape as X.", "T")
        .Output(
            1,
            "mean",
            "The running mean after the BatchNormalization operator. Must be in-place "
            "with the input mean. Should not be used for testing.",
            "T",
            OpSchema::Optional)
        .Output(
            2,
            "var",
            "The running variance after the BatchNormalization operator. Must be "
            "in-place with the input var. Should not be used for testing.",
            "T",
            OpSchema::Optional)
        .Output(
            3,
            "saved_mean",
            "Saved mean used during training to speed up gradient "
            "computation. Should not be used for testing.",
            "T",
            OpSchema::Optional)
        .Output(
            4,
            "saved_var",
            "Saved variance used during training to speed up "
            "gradient computation. Should not be used for testing.",
            "T",
            OpSchema::Optional)
        .TypeConstraint(
            "T",
            {"tensor(float16)", "tensor(float)", "tensor(double)"},
            "Constrain input and output types to float tensors.")
        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
          propagateShapeAndTypeFromFirstInput(ctx);
          // TODO in training mode, it may be possible to infer some of
          // the other outputs as well.
        }));

static const char* Flatten_ver1_doc = R"DOC(
Flattens the input tensor into a 2D matrix. If input tensor has shape
(d_0, d_1, ... d_n) then the output will have shape
(d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn).
)DOC";

ONNX_OPERATOR_SET_SCHEMA(
    Flatten,
    1,
    OpSchema()
        .SetDoc(Flatten_ver1_doc)
        .Input(0, "input", "A tensor of rank >= axis.", "T")
        .Output(
            0,
            "output",
            "A 2D tensor with the contents of the input tensor, "
            "with input dimensions up to axis flattened to the outer dimension "
            "of the output and remaining input dimensions flattened into the inner "
            "dimension of the output.",
            "T")
        .TypeConstraint(
            "T",
            {"tensor(float16)", "tensor(float)", "tensor(double)"},
            "Constrain input and output types to float tensors.")
        .Attr(
            "axis",
            "Indicate up to which input dimensions "
            "(exclusive) should be flattened to the outer dimension of the output. "
            "The value for axis must be in the range [0, R], where R is the rank of the input tensor. "
            "When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), "
            "where the shape of the input tensor is (d_0, d_1, ... d_n). ",
            AttributeProto::INT,
            static_cast<int64_t>(1))
        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
          propagateElemTypeFromInputToOutput(ctx, 0, 0);
          if (!hasInputShape(ctx, 0))
            return;
          auto& input_shape = getInputShape(ctx, 0);
          int rank = static_cast<int>(input_shape.dim_size());
          int axis = static_cast<int>(getAttribute(ctx, "axis", 1));
          if (axis > rank || axis < 0) {
            fail_shape_inference(
                "Invalid value(", axis, ") for attribute 'axis'");
          }
          // TODO: is the operation defined for input-rank < 2?
          updateOutputShape(
              ctx,
              0,
              {multiplyDims(input_shape, 0, axis),
               multiplyDims(input_shape, axis, rank)});
        }));

static const char* Flatten_ver9_doc = R"DOC(
Flattens the input tensor into a 2D matrix. If input tensor has shape
(d_0, d_1, ... d_n) then the output will have shape
(d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn).
)DOC";

ONNX_OPERATOR_SET_SCHEMA(
    Flatten,
    9,
    OpSchema()
        .SetDoc(Flatten_ver9_doc)
        .Input(0, "input", "A tensor of rank >= axis.", "T")
        .Output(
            0,
            "output",
            "A 2D tensor with the contents of the input tensor, "
            "with input dimensions up to axis flattened to the outer dimension "
            "of the output and remaining input dimensions flattened into the inner "
            "dimension of the output.",
            "T")
        .TypeConstraint(
            "T",
            OpSchema::all_tensor_types(),
            "Constrain input and output to all tensor types.")
        .Attr(
            "axis",
            "Indicate up to which input dimensions "
            "(exclusive) should be flattened to the outer dimension of the output. "
            "The value for axis must be in the range [0, R], where R is the rank of the input tensor. "
            "When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), "
            "where the shape of the input tensor is (d_0, d_1, ... d_n). ",
            AttributeProto::INT,
            static_cast<int64_t>(1))
        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
          propagateElemTypeFromInputToOutput(ctx, 0, 0);
          if (!hasInputShape(ctx, 0))
            return;
          auto& input_shape = getInputShape(ctx, 0);
          int rank = static_cast<int>(input_shape.dim_size());
          int axis = static_cast<int>(getAttribute(ctx, "axis", 1));
          if (axis > rank || axis < 0) {
            fail_shape_inference(
                "Invalid value(", axis, ") for attribute 'axis'");
          }
          // TODO: is the operation defined for input-rank < 2?
          updateOutputShape(
              ctx,
              0,
              {multiplyDims(input_shape, 0, axis),
               multiplyDims(input_shape, axis, rank)});
        }));

static const char* BatchNormalization_ver7_doc = R"DOC(
    Carries out batch normalization as described in the paper
    https://arxiv.org/abs/1502.03167. Depending on the mode it is being run,
    there are multiple cases for the number of outputs, which we list below:

    Output case #1: Y, mean, var, saved_mean, saved_var (training mode)
    Output case #2: Y (test mode)
        )DOC";

ONNX_OPERATOR_SET_SCHEMA(
    BatchNormalization,
    7,
    OpSchema()
        .SetDoc(GET_OP_DOC_STR(
            std::string(BatchNormalization_ver7_doc) +
            GenerateOptionalArgumentsDoc()))
        .NumOutputs({1, 5})
        .Attr(
            "spatial",
            "If true, compute the mean and variance across per activation. "
            "If false, compute the mean and variance across per feature over "
            "each mini-batch.",
            AttributeProto::INT,
            static_cast<int64_t>(1))
        .Attr(
            "epsilon",
            "The epsilon value to use to avoid division by zero.",
            AttributeProto::FLOAT,
            1e-5f)
        .Attr(
            "momentum",
            "Factor used in computing the running mean and variance."
            "e.g., running_mean = running_mean * momentum + mean * (1 - momentum).",
            AttributeProto::FLOAT,
            0.9f)
        .Input(
            0,
            "X",
            "Input data tensor from the previous operator; "
            "dimensions for image case are (N x C x H x W), "
            "where N is the batch size, C is the number of "
            "channels, and H and W are the height and the "
            "width of the data. For non image case, the "
            "dimensions are in the form of "
            "(N x C x D1 x D2 ... Dn), where N is the batch "
            "size.",
            "T")
        .Input(
            1,
            "scale",
            "If spatial is true, the dimension of scale is (C). "
            "If spatial is false, the dimensions of scale are "
            "(C x D1 x ... x Dn)",
            "T")
        .Input(
            2,
            "B",
            "If spatial is true, the dimension of bias is (C). "
            "If spatial is false, the dimensions of bias are "
            "(C x D1 x ... x Dn)",
            "T")
        .Input(
            3,
            "mean",
            "If spatial is true, the dimension of the running mean "
            "(training) or the estimated mean (testing) is (C). "
            "If spatial is false, the dimensions of the running mean "
            "(training) or the estimated mean (testing) are (C x D1 x ... x Dn).",
            "T")
        .Input(
            4,
            "var",
            "If spatial is true, the dimension of the running variance"
            "(training) or the estimated variance (testing) is (C). "
            "If spatial is false, the dimensions of the running variance"
            "(training) or the estimated variance (testing) are (C x D1 x ... x Dn).",
            "T")
        .Output(0, "Y", "The output tensor of the same shape as X", "T")
        .Output(
            1,
            "mean",
            "The running mean after the BatchNormalization operator.",
            "T",
            OpSchema::Optional)
        .Output(
            2,
            "var",
            "The running variance after the BatchNormalization operator.",
            "T",
            OpSchema::Optional)
        .Output(
            3,
            "saved_mean",
            "Saved mean used during training to speed up gradient "
            "computation.",
            "T",
            OpSchema::Optional)
        .Output(
            4,
            "saved_var",
            "Saved variance used during training to speed up "
            "gradient computation.",
            "T",
            OpSchema::Optional)
        .TypeConstraint(
            "T",
            {"tensor(float16)", "tensor(float)", "tensor(double)"},
            "Constrain input and output types to float tensors.")
        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
          propagateShapeAndTypeFromFirstInput(ctx);
          // TODO in training mode, it may be possible to infer some of
          // the other outputs as well.
        }));
} // namespace ONNX_NAMESPACE
