onnx-mlir/src/dialect/onnx/onnxop.inc

def ONNXAbsOp:ONNX_Op<"Abs", 
    [NoSideEffect]> {
  let summary = "ONNX Abs operation";
  let description = [{
    "Absolute takes one input data (Tensor<T>) and produces one output data"
    "(Tensor<T>) where the absolute is, y = abs(x), is applied to"
    "the tensor elementwise."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXAcosOp:ONNX_Op<"Acos", 
    [NoSideEffect]> {
  let summary = "ONNX Acos operation";
  let description = [{
    "Calculates the arccosine (inverse of cosine) of the given input tensor, element-wise."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXAcoshOp:ONNX_Op<"Acosh", 
    [NoSideEffect]> {
  let summary = "ONNX Acosh operation";
  let description = [{
    "Calculates the hyperbolic arccosine of the given input tensor element-wise."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXAddOp:ONNX_Op<"Add", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let hasCanonicalizer = 1;
  let summary = "ONNX Add operation";
  let description = [{
    "Performs element-wise binary addition (with Numpy-style broadcasting support)."
    ""
    "This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md)."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$A, AnyTypeOf<[AnyMemRef, AnyTensor]>:$B);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXAndOp:ONNX_Op<"And", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let summary = "ONNX And operation";
  let description = [{
    "Returns the tensor resulted from performing the `and` logical operation"
    "elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support)."
    ""
    "This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md)."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$A, AnyTypeOf<[AnyMemRef, AnyTensor]>:$B);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXArgMaxOp:ONNX_Op<"ArgMax", 
    [NoSideEffect]> {
  let summary = "ONNX ArgMax operation";
  let description = [{
    "Computes the indices of the max elements of the input tensor's element along the "
    "provided axis. The resulting tensor has the same rank as the input if keepdims equal 1. "
    "If keepdims equal 0, then the resulting tensor have the reduced dimension pruned. "
    "If select_last_index is True (default False), the index of the last occurence of the max "
    "is selected if the max appears more than once in the input. Otherwise the index of the "
    "first occurence is selected."
    "The type of the output tensor is integer."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXArgMinOp:ONNX_Op<"ArgMin", 
    [NoSideEffect]> {
  let summary = "ONNX ArgMin operation";
  let description = [{
    "Computes the indices of the min elements of the input tensor's element along the "
    "provided axis. The resulting tensor has the same rank as the input if keepdims equal 1. "
    "If keepdims equal 0, then the resulting tensor have the reduced dimension pruned. "
    "If select_last_index is True (default False), the index of the last occurence of the min "
    "is selected if the min appears more than once in the input. Otherwise the index of the "
    "first occurence is selected."
    "The type of the output tensor is integer."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXAsinOp:ONNX_Op<"Asin", 
    [NoSideEffect]> {
  let summary = "ONNX Asin operation";
  let description = [{
    "Calculates the arcsine (inverse of sine) of the given input tensor, element-wise."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXAsinhOp:ONNX_Op<"Asinh", 
    [NoSideEffect]> {
  let summary = "ONNX Asinh operation";
  let description = [{
    "Calculates the hyperbolic arcsine of the given input tensor element-wise."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXAtanOp:ONNX_Op<"Atan", 
    [NoSideEffect]> {
  let summary = "ONNX Atan operation";
  let description = [{
    "Calculates the arctangent (inverse of tangent) of the given input tensor, element-wise."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXAtanhOp:ONNX_Op<"Atanh", 
    [NoSideEffect]> {
  let summary = "ONNX Atanh operation";
  let description = [{
    "Calculates the hyperbolic arctangent of the given input tensor element-wise."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXAveragePoolOp:ONNX_Op<"AveragePool", 
    [NoSideEffect]> {
  let summary = "ONNX AveragePool operation";
  let description = [{
    "AveragePool consumes an input tensor X and applies average pooling across"
    " the tensor according to kernel sizes, stride sizes, and pad lengths."
    " average pooling consisting of computing the average 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)"
    " ```"
    " or"
    " ```"
    " output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / 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] - 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]"
    " ```"
    " The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero)."
    " "
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXBatchNormalizationOp:ONNX_Op<"BatchNormalization", 
    [NoSideEffect]> {
  let summary = "ONNX BatchNormalization operation";
  let description = [{
    "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."
    "This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X, AnyTypeOf<[AnyMemRef, AnyTensor]>:$scale, AnyTypeOf<[AnyMemRef, AnyTensor]>:$B, AnyTypeOf<[AnyMemRef, AnyTensor]>:$mean, AnyTypeOf<[AnyMemRef, AnyTensor]>:$var);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>, AnyTypeOf<[AnyMemRef, AnyTensor]>, AnyTypeOf<[AnyMemRef, AnyTensor]>, AnyTypeOf<[AnyMemRef, AnyTensor]>, AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXBitShiftOp:ONNX_Op<"BitShift", 
    [NoSideEffect]> {
  let summary = "ONNX BitShift operation";
  let description = [{
    "Bitwise shift operator performs element-wise operation. For each input element, if the"
    " attribute "direction" is "RIGHT", this operator moves its binary representation toward"
    " the right side so that the input value is effectively decreased. If the attribute "direction""
    " is "LEFT", bits of binary representation moves toward the left side, which results the"
    " increase of its actual value. The input X is the tensor to be shifted and another input"
    " Y specifies the amounts of shifting. For example, if "direction" is "Right", X is [1, 4],"
    " and S is [1, 1], the corresponding output Z would be [0, 2]. If "direction" is "LEFT" with"
    " X=[1, 2] and S=[1, 2], the corresponding output Y would be [2, 8]."
    " "
    " Because this operator supports Numpy-style broadcasting, X's and Y's shapes are"
    " not necessarily identical."
    "This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md)."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X, AnyTypeOf<[AnyMemRef, AnyTensor]>:$Y);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXCastOp:ONNX_Op<"Cast", 
    [NoSideEffect]> {
  let summary = "ONNX Cast operation";
  let description = [{
    "The operator casts the elements of a given input tensor to a data type"
    "specified by the 'to' argument and returns an output tensor of the same size in"
    "the converted type. The 'to' argument must be one of the data types specified"
    "in the 'DataType' enum field in the TensorProto message."
    ""
    "Casting from string tensor in plain (e.g., "3.14" and "1000") and scientific numeric representations"
    "(e.g., "1e-5" and "1E8") to float types is supported. For example, converting string "100.5" to an integer may"
    "result 100. There are some string literals reserved for special floating-point values;"
    ""+INF" (and "INF"), "-INF", and "NaN" are positive infinity, negative infinity, and not-a-number, respectively."
    "Any string which can exactly match "+INF" in a case-insensitive way would be mapped to positive infinite. Similarly,"
    "this case-insensitive rule is applied to "INF" and "NaN". When casting from numeric tensors"
    "to string tensors, plain floating-point representation (such as "314.15926") would be used. "
    "Converting non-numerical-literal string such as "Hello World!" is an undefined behavior. Cases "
    "of converting string representing floating-point arithmetic value, such as "2.718", to INT is an undefined behavior."
    ""
    "Conversion from a numerical type to any numerical type is always allowed."
    "User must be aware of precision loss and value change caused by range difference between two types."
    "For example, a 64-bit float 3.1415926459 may be round to a 32-bit float 3.141592. Similarly, converting"
    "an integer 36 to Boolean may produce 1 because we truncate bits which can't be stored in the targeted type."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXCeilOp:ONNX_Op<"Ceil", 
    [NoSideEffect]> {
  let summary = "ONNX Ceil operation";
  let description = [{
    "Ceil takes one input data (Tensor<T>) and produces one output data"
    "(Tensor<T>) where the ceil is, y = ceil(x), is applied to"
    "the tensor elementwise."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXClipOp:ONNX_Op<"Clip", 
    [NoSideEffect]> {
  let summary = "ONNX Clip operation";
  let description = [{
    "Clip operator limits the given input within an interval. The interval is"
    "specified by the inputs 'min' and 'max'. They default to"
    "numeric_limits::lowest() and numeric_limits::max(), respectively."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input, AnyTypeOf<[AnyMemRef, AnyTensor]>:$min, AnyTypeOf<[AnyMemRef, AnyTensor]>:$max);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXCompressOp:ONNX_Op<"Compress", 
    [NoSideEffect]> {
  let summary = "ONNX Compress operation";
  let description = [{
    "Selects slices from an input tensor along a given axis where condition evaluates to True for each axis index."
    "    In case axis is not provided, input is flattened before elements are selected."
    "    Compress behaves like numpy.compress: https://docs.scipy.org/doc/numpy/reference/generated/numpy.compress.html"
    "    "
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input, AnyTypeOf<[AnyMemRef, AnyTensor]>:$condition);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXConcatOp:ONNX_Op<"Concat", 
    [NoSideEffect]> {
  let summary = "ONNX Concat operation";
  let description = [{
    "Concatenate a list of tensors into a single tensor. All input tensors must have the same shape, except for the dimension size of the axis to concatenate on."
  }];
  let arguments = (ins Variadic<AnyTypeOf<[AnyMemRef, AnyTensor]>>:$inputs);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXConcatFromSequenceOp:ONNX_Op<"ConcatFromSequence", 
    [NoSideEffect]> {
  let summary = "ONNX ConcatFromSequence operation";
  let description = [{
    "Concatenate a sequence of tensors into a single tensor."
    "All input tensors must have the same shape, except for the dimension size of the axis to concatenate on."
    "By default 'new_axis' is 0, the behavior is similar to numpy.concatenate."
    "When 'new_axis' is 1, the behavior is similar to numpy.stack."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input_sequence);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXConstantOp:ONNX_Op<"Constant", 
    [NoSideEffect]> {
  let summary = "ONNX Constant operation";
  let description = [{
    "A constant tensor. Exactly one of the two attributes, either value or sparse_value,"
    "must be specified."
  }];
  let arguments = (ins );
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXConstantOfShapeOp:ONNX_Op<"ConstantOfShape", 
    [NoSideEffect]> {
  let summary = "ONNX ConstantOfShape operation";
  let description = [{
    "Generate a tensor with given value and shape."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXConvOp:ONNX_Op<"Conv",
    [NoSideEffect]> {
  let summary = "ONNX Conv operation";
  let description = [{
    "The convolution operator consumes an input tensor and a filter, and"
    "computes the output."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X, AnyTypeOf<[AnyMemRef, AnyTensor]>:$W, AnyTypeOf<[AnyMemRef, AnyTensor]>:$B);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);

  let extraClassDeclaration = [{
     static StringRef getAutoPadAttrName() { return "auto_pad"; }
     static StringRef getDilationsAttrName() { return "dilations"; }
     static StringRef getGroupAttrName() { return "group"; }
     static StringRef getKernelShapeAttrName() { return "kernel_shape"; }
     static StringRef getPadsAttrName() { return "pads"; }
     static StringRef getStridesAttrName() { return "strides"; }
  }];
}

def ONNXConvIntegerOp:ONNX_Op<"ConvInteger", 
    [NoSideEffect]> {
  let summary = "ONNX ConvInteger operation";
  let description = [{
    "The integer convolution operator consumes an input tensor, its zero-point, a filter, and its zero-point,"
    "and computes the output. The production MUST never overflow. The accumulation may overflow if and only if in 32 bits."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$x, AnyTypeOf<[AnyMemRef, AnyTensor]>:$w, AnyTypeOf<[AnyMemRef, AnyTensor]>:$x_zero_point, AnyTypeOf<[AnyMemRef, AnyTensor]>:$w_zero_point);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXConvTransposeOp:ONNX_Op<"ConvTranspose", 
    [NoSideEffect]> {
  let summary = "ONNX ConvTranspose operation";
  let description = [{
    "The convolution transpose operator consumes an input tensor and a filter,"
    "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)."
    ""
    "    "
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X, AnyTypeOf<[AnyMemRef, AnyTensor]>:$W, AnyTypeOf<[AnyMemRef, AnyTensor]>:$B);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXCosOp:ONNX_Op<"Cos", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let summary = "ONNX Cos operation";
  let description = [{
    "Calculates the cosine of the given input tensor, element-wise."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXCoshOp:ONNX_Op<"Cosh", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let summary = "ONNX Cosh operation";
  let description = [{
    "Calculates the hyperbolic cosine of the given input tensor element-wise."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXCumSumOp:ONNX_Op<"CumSum", 
    [NoSideEffect]> {
  let summary = "ONNX CumSum operation";
  let description = [{
    "Performs cumulative sum of the input elements along the given axis."
    "By default, it will do the sum inclusively meaning the first element is copied as is."
    "Through an `exclusive` attribute, this behavior can change to exclude the first element."
    "It can also perform summation in the opposite direction of the axis. For that, set `reverse` attribute to 1."
    ""
    "Example:"
    "```"
    "input_x = [1, 2, 3]"
    "axis=0"
    "output = [1, 3, 6]"
    "exclusive=1"
    "output = [0, 1, 3]"
    "exclusive=0"
    "reverse=1"
    "output = [6, 5, 3]"
    "exclusive=1"
    "reverse=1"
    "output = [5, 3, 0]"
    "```"
    " "
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$x, AnyTypeOf<[AnyMemRef, AnyTensor]>:$axis);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXDepthToSpaceOp:ONNX_Op<"DepthToSpace", 
    [NoSideEffect]> {
  let summary = "ONNX DepthToSpace operation";
  let description = [{
    "DepthToSpace rearranges (permutes) data from depth into blocks of spatial data."
    "This is the reverse transformation of SpaceToDepth. More specifically, this op outputs a copy of"
    "the input tensor where values from the depth dimension are moved in spatial blocks to the height"
    "and width dimensions. By default, `mode` = `DCR`."
    "In the DCR mode, elements along the depth dimension from the input tensor are rearranged in the"
    "following order: depth, column, and then row. The output y is computed from the input x as below:"
    ""
    "b, c, h, w = x.shape"
    ""
    "tmp = np.reshape(x, [b, blocksize, blocksize, c // (blocksize**2), h, w])"
    ""
    "tmp = np.transpose(tmp, [0, 3, 4, 1, 5, 2])"
    ""
    "y = np.reshape(tmp, [b, c // (blocksize**2), h * blocksize, w * blocksize])"
    ""
    ""
    "In the CRD mode, elements along the depth dimension from the input tensor are rearranged in the"
    "following order: column, row, and the depth. The output y is computed from the input x as below:"
    ""
    "b, c, h, w = x.shape"
    ""
    "tmp = np.reshape(x, [b, c // (blocksize ** 2), blocksize, blocksize, h, w])"
    ""
    "tmp = np.transpose(tmp, [0, 1, 4, 2, 5, 3])"
    ""
    "y = np.reshape(tmp, [b, c // (blocksize ** 2), h * blocksize, w * blocksize])"
    ""
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXDequantizeLinearOp:ONNX_Op<"DequantizeLinear", 
    [NoSideEffect]> {
  let summary = "ONNX DequantizeLinear operation";
  let description = [{
    "The linear dequantization operator. It consumes a quantized tensor, a scale, a zero point to compute the full precision tensor."
    "The dequantization formula is y = (x - x_zero_point) * x_scale. 'x_scale' and 'x_zero_point' must have same shape."
    "'x_zero_point' and 'x' must have same type. 'x' and 'y' must have same shape. In the case of dequantizing int32,"
    "there's no zero point (zero point is supposed to be 0)."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$x, AnyTypeOf<[AnyMemRef, AnyTensor]>:$x_scale, AnyTypeOf<[AnyMemRef, AnyTensor]>:$x_zero_point);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXDetOp:ONNX_Op<"Det", 
    [NoSideEffect]> {
  let summary = "ONNX Det operation";
  let description = [{
    "Det calculates determinant of a square matrix or batches of square matrices."
    "Det takes one input tensor of shape `[*, M, M]`, where `*` is zero or more batch dimensions,"
    "and the inner-most 2 dimensions form square matrices."
    "The output is a tensor of shape `[*]`, containing the determinants of all input submatrices."
    "e.g., When the input is 2-D, the output is a scalar(shape is empty: `[]`)."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXDivOp:ONNX_Op<"Div", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let summary = "ONNX Div operation";
  let description = [{
    "Performs element-wise binary division (with Numpy-style broadcasting support)."
    ""
    "This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md)."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$A, AnyTypeOf<[AnyMemRef, AnyTensor]>:$B);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXDropoutOp:ONNX_Op<"Dropout", 
    [NoSideEffect]> {
  let summary = "ONNX Dropout operation";
  let description = [{
    "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."
    "This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>, AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXDynamicQuantizeLinearOp:ONNX_Op<"DynamicQuantizeLinear", 
    [NoSideEffect]> {
  let summary = "ONNX DynamicQuantizeLinear operation";
  let description = [{
    "A Function to fuse calculation for Scale, Zero Point and FP32->8Bit convertion of FP32 Input data."
    "Outputs Scale, ZeroPoint and Quantized Input for a given FP32 Input."
    "Scale is calculated as:"
    "```"
    " y_scale = (max(x) - min(x))/(qmax - qmin)"
    " * where qmax and qmin are max and min values for quantization range .i.e [0, 255] in case of uint8"
    " * data range is adjusted to include 0."
    "```"
    "Zero point is calculated as:"
    "```"
    "intermediate_zero_point = (qmin - min(x))/(qmax - qmin)"
    "y_zero_point = cast(round(saturate(itermediate_zero_point)))"
    "* where qmax and qmin are max and min values for quantization range .i.e [0, 255] in case of uint8"
    "* for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported."
    "* rounding to nearest ties to even."
    "```"
    "Data quantization formula is:"
    "```"
    "y = saturate (round (x / y_scale) + y_zero_point)"
    "* for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported."
    "* rounding to nearest ties to even."
    "```"
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$x);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>, AnyTypeOf<[AnyMemRef, AnyTensor]>, AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXEluOp:ONNX_Op<"Elu", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let summary = "ONNX Elu operation";
  let description = [{
    "Elu takes one input data (Tensor<T>) and produces one output data"
    "(Tensor<T>) where the function `f(x) = alpha * (exp(x) - 1.) for x <"
    "0`, `f(x) = x for x >= 0`., is applied to the tensor elementwise."
    ""
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXEqualOp:ONNX_Op<"Equal", 
    [NoSideEffect]> {
  let summary = "ONNX Equal operation";
  let description = [{
    "Returns the tensor resulted from performing the `equal` logical operation"
    "elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support)."
    ""
    "This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md)."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$A, AnyTypeOf<[AnyMemRef, AnyTensor]>:$B);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXErfOp:ONNX_Op<"Erf", 
    [NoSideEffect]> {
  let summary = "ONNX Erf operation";
  let description = [{
    "Computes the error function of the given input tensor element-wise."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXExpOp:ONNX_Op<"Exp", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let summary = "ONNX Exp operation";
  let description = [{
    "Calculates the exponential of the given input tensor, element-wise."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXExpandOp:ONNX_Op<"Expand", 
    [NoSideEffect]> {
  let summary = "ONNX Expand operation";
  let description = [{
    "Broadcast the input tensor following the given shape and the broadcast rule."
    "The broadcast rule is similar to numpy.array(input) * numpy.ones(shape):"
    "Dimensions are right alignment;"
    "Two corresponding dimension must have the same value, or one of them is equal to 1."
    "Also, this operator is similar to numpy.broadcast_to(input, shape),"
    "but the major difference is numpy.broadcast_to() does not allow shape to be smaller than input.size()."
    "It is possible that the output.shape is not equal to shape, when some dimensions in shape is equal to 1,"
    "or the shape.ndim < input.shape.ndim."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input, AnyTypeOf<[AnyMemRef, AnyTensor]>:$shape);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXEyeLikeOp:ONNX_Op<"EyeLike", 
    [NoSideEffect]> {
  let summary = "ONNX EyeLike operation";
  let description = [{
    "Generate a 2D tensor (matrix) with ones on the diagonal and zeros everywhere else. Only 2D"
    "tensors are supported, i.e. input T1 must be of rank 2. The shape of the output tensor is the"
    "same as the input tensor. The data type can be specified by the 'dtype' argument. If"
    "'dtype' is not specified, then the type of input tensor is used. By default, the main diagonal"
    "is populated with ones, but attribute 'k' can be used to populate upper or lower diagonals."
    "The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the"
    "TensorProto message and be valid as an output type."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXFlattenOp:ONNX_Op<"Flatten", 
    [NoSideEffect]> {
  let summary = "ONNX Flatten operation";
  let description = [{
    "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)."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXFloorOp:ONNX_Op<"Floor", 
    [NoSideEffect]> {
  let summary = "ONNX Floor operation";
  let description = [{
    "Floor takes one input data (Tensor<T>) and produces one output data"
    "(Tensor<T>) where the floor is, y = floor(x), is applied to"
    "the tensor elementwise."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXGRUOp:ONNX_Op<"GRU", 
    [NoSideEffect]> {
  let summary = "ONNX GRU operation";
  let description = [{
    "Computes an one-layer GRU. This operator is usually supported via some custom"
    "implementation such as CuDNN."
    ""
    "Notations:"
    ""
    "`X` - input tensor"
    ""
    "`z` - update gate"
    ""
    "`r` - reset gate"
    ""
    "`h` - hidden gate"
    ""
    "`t` - time step (t-1 means previous time step)"
    ""
    "`W[zrh]` - W parameter weight matrix for update, reset, and hidden gates"
    ""
    "`R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates"
    ""
    "`Wb[zrh]` - W bias vectors for update, reset, and hidden gates"
    ""
    "`Rb[zrh]` - R bias vectors for update, reset, and hidden gates"
    ""
    "`WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates"
    ""
    "`RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates"
    ""
    "`WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates"
    ""
    "`RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates"
    ""
    "`H` - Hidden state"
    ""
    "`num_directions` - 2 if direction == bidirectional else 1"
    ""
    "Activation functions:"
    ""
    "  Relu(x)                - max(0, x)"
    ""
    "  Tanh(x)                - (1 - e^{-2x})/(1 + e^{-2x})"
    ""
    "  Sigmoid(x)             - 1/(1 + e^{-x})"
    ""
    "  (NOTE: Below are optional)"
    ""
    "  Affine(x)              - alpha*x + beta"
    ""
    "  LeakyRelu(x)           - x if x >= 0 else alpha * x"
    ""
    "  ThresholdedRelu(x)     - x if x >= alpha else 0"
    ""
    "  ScaledTanh(x)          - alpha*Tanh(beta*x)"
    ""
    "  HardSigmoid(x)         - min(max(alpha*x + beta, 0), 1)"
    ""
    "  Elu(x)                 - x if x >= 0 else alpha*(e^x - 1)"
    ""
    "  Softsign(x)            - x/(1 + |x|)"
    ""
    "  Softplus(x)            - log(1 + e^x)"
    ""
    "Equations (Default: f=Sigmoid, g=Tanh):"
    ""
    "  - zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz)"
    ""
    "  - rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr)"
    ""
    "  - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0"
    ""
    "  - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0"
    ""
    "  - Ht = (1 - zt) (.) ht + zt (.) Ht-1"
    "This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X, AnyTypeOf<[AnyMemRef, AnyTensor]>:$W, AnyTypeOf<[AnyMemRef, AnyTensor]>:$R, AnyTypeOf<[AnyMemRef, AnyTensor]>:$B, AnyTypeOf<[AnyMemRef, AnyTensor]>:$sequence_lens, AnyTypeOf<[AnyMemRef, AnyTensor]>:$initial_h);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>, AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXGatherOp:ONNX_Op<"Gather", 
    [NoSideEffect]> {
  let summary = "ONNX Gather operation";
  let description = [{
    "Given `data` tensor of rank r >= 1, and `indices` tensor of rank q, gather"
    "entries of the axis dimension of `data` (by default outer-most one as axis=0) indexed by `indices`, and concatenates"
    "them in an output tensor of rank q + (r - 1)."
    ""
    "axis = 0 :"
    ""
    "Let"
    "k = indices[i_{0}, ..., i_{q-1\}\]"
    "Then"
    "output[i_{0}, ..., i_{q-1}, j_{0}, ..., j_{r-2\}\] = input[k , j_{0}, ..., j_{r-2\}\]"
    ""
    "```"
    "  data = ["
    "      [1.0, 1.2],"
    "      [2.3, 3.4],"
    "      [4.5, 5.7],"
    "  ]"
    "  indices = ["
    "      [0, 1],"
    "      [1, 2],"
    "  ]"
    "  output = ["
    "      ["
    "          [1.0, 1.2],"
    "          [2.3, 3.4],"
    "      ],"
    "      ["
    "          [2.3, 3.4],"
    "          [4.5, 5.7],"
    "      ],"
    "  ]"
    "```"
    "axis = 1 :"
    ""
    "Let"
    "k = indices[i_{0}, ..., i_{q-1\}\]"
    "Then"
    "output[i_{0}, ..., i_{q-1}, j_{0}, ..., j_{r-2\}\] = input[j_{0}, k, j_{1}, ..., j_{r-2\}\]"
    ""
    "```"
    "  data = ["
    "      [1.0, 1.2, 1.9],"
    "      [2.3, 3.4, 3.9],"
    "      [4.5, 5.7, 5.9],"
    "  ]"
    "  indices = ["
    "      [0, 2],"
    "  ]"
    "  axis = 1,"
    "  output = ["
    "      ["
    "          [1.0, 1.9],"
    "          [2.3, 3.9],"
    "          [4.5, 5.9],"
    "      ],"
    "  ]"
    "```"
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data, AnyTypeOf<[AnyMemRef, AnyTensor]>:$indices);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXGatherElementsOp:ONNX_Op<"GatherElements", 
    [NoSideEffect]> {
  let summary = "ONNX GatherElements operation";
  let description = [{
    "GatherElements takes two inputs `data` and `indices` of the same rank r >= 1"
    "and an optional attribute `axis` that identifies an axis of `data`"
    "(by default, the outer-most axis, that is axis 0). It is an indexing operation"
    "that produces its output by indexing into the input data tensor at index"
    "positions determined by elements of the `indices` tensor."
    "Its output shape is the same as the shape of `indices` and consists of one value"
    "(gathered from the `data`) for each element in `indices`."
    ""
    "For instance, in the 3-D case (r = 3), the output produced is determined"
    "by the following equations: "
    "```"
    "  out[i][j][k] = input[index[i][j][k]][j][k] if axis = 0,"
    "  out[i][j][k] = input[i][index[i][j][k]][k] if axis = 1,"
    "  out[i][j][k] = input[i][j][index[i][j][k]] if axis = 2,"
    "```"
    ""
    "This operator is also the inverse of ScatterElements. It is similar to Torch's gather operation."
    ""
    "Example 1:"
    "```"
    "  data = ["
    "      [1, 2],"
    "      [3, 4],"
    "  ]"
    "  indices = ["
    "      [0, 0],"
    "      [1, 0],"
    "  ]"
    "  axis = 1"
    "  output = ["
    "      ["
    "        [1, 1],"
    "        [4, 3],"
    "      ],"
    "  ]"
    "```"
    "Example 2:"
    "```"
    "  data = ["
    "      [1, 2, 3],"
    "      [4, 5, 6],"
    "      [7, 8, 9],"
    "  ]"
    "  indices = ["
    "      [1, 2, 0],"
    "      [2, 0, 0],"
    "  ]"
    "  axis = 0"
    "  output = ["
    "      ["
    "        [4, 8, 3],"
    "        [7, 2, 3],"
    "      ],"
    "  ]"
    "```"
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data, AnyTypeOf<[AnyMemRef, AnyTensor]>:$indices);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXGatherNDOp:ONNX_Op<"GatherND", 
    [NoSideEffect]> {
  let summary = "ONNX GatherND operation";
  let description = [{
    "Given `data` tensor of rank `r` >= 1, and `indices` tensor of rank `q` >= 1, this operator gathers "
    "slices of `data` into an output tensor of rank `q + r - indices_shape[-1] - 1`."
    ""
    "`indices` is an q-dimensional integer tensor, best thought of as a `(q-1)`-dimensional tensor of index-tuples into `data`, "
    "where each element defines a slice of `data`"
    ""
    "Some salient points about the inputs' rank and shape:"
    " "
    "1) r >= 1 and q >= 1 are to be honored. There is no dependency condition to be met between ranks `r` and `q`"
    ""
    "2) The `indices_shape[-1]` should have a value between 1 (inclusive) and rank `r` (inclusive) "
    ""
    "3) All values in `indices` are expected to be within bounds [-s, s-1] along axis of size `s` (i.e.) `-data_shape[i] <= indices[...,i] <= data_shape[i] - 1`."
    "   It is an error if any of the index values are out of bounds."
    ""
    "The output is computed as follows:"
    ""
    "The output tensor is obtained by mapping each index-tuple in the `indices` tensor to the corresponding slice of the input `data`."
    " "
    "1) If `indices_shape[-1] > r` => error condition"
    ""
    "2) If `indices_shape[-1] == r`, since the rank of `indices` is `q`, `indices` can be thought of as a `(q-1)`-dimensional tensor"
    "   containing 1-D tensors of dimension `r`. Let us think of each such `r` ranked tensor as `indices_slice`. "
    "   Each *scalar value* corresponding to `data[indices_slice]` is filled into the corresponding location of the `(q-1)`-dimensional tensor "
    "   to form the `output` tensor (Example 1 below)"
    ""
    "3) If `indices_shape[-1] < r`, since the rank of `indices` is `q`, `indices` can be thought of as a `(q-1)`-dimensional tensor"
    "   containing 1-D tensors of dimension `< r`. Let us think of each such tensors as `indices_slice`. "
    "   Each *tensor slice* corresponding to `data[indices_slice , :]` is filled into the corresponding location of the `(q-1)`-dimensional tensor "
    "   to form the `output` tensor (Examples 2, 3, and 4 below)"
    ""
    "This operator is the inverse of `ScatterND`."
    ""
    "`Example 1`"
    ""
    "  data    = [[0,1],[2,3]]   # data_shape = [2, 2]"
    ""
    "  indices = [[0,0],[1,1]]   # indices_shape = [2, 2]"
    ""
    "  output  = [0,3]           # output_shape = [2]"
    ""
    "`Example 2`"
    ""
    "  data    = [[0,1],[2,3]]  # data_shape = [2, 2]"
    ""
    "  indices = [[1],[0]]      # indices_shape = [2, 1]"
    ""
    "  output  = [[2,3],[0,1]]  # output_shape = [2, 2]"
    ""
    "`Example 3`"
    ""
    "  data    = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2]"
    ""
    "  indices = [[0,1],[1,0]]                 # indices_shape = [2, 2]"
    ""
    "  output  = [[2,3],[4,5]]                 # output_shape = [2, 2]   "
    ""
    "`Example 4`"
    ""
    "  data    = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2]"
    ""
    "  indices = [[[0,1]],[[1,0]]]             # indices_shape = [2, 1, 2]"
    ""
    "  output  = [[[2,3]],[[4,5]]]             # output_shape = [2, 1, 2] "
    ""
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data, AnyTypeOf<[AnyMemRef, AnyTensor]>:$indices);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXGemmOp:ONNX_Op<"Gemm", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let summary = "ONNX Gemm operation";
  let description = [{
    "General Matrix multiplication:"
    "https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3"
    ""
    "A' = transpose(A) if transA else A"
    ""
    "B' = transpose(B) if transB else B"
    ""
    "Compute Y = alpha * A' * B' + beta * C, where input tensor A has shape (M, K) or (K, M),"
    "input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N),"
    "and output tensor Y has shape (M, N). A will be transposed before doing the"
    "computation if attribute transA is non-zero, same for B and transB."
    "This operator supports **unidirectional broadcasting** (tensor C should be unidirectional broadcastable to tensor A * B); for more details please check [the doc](Broadcasting.md)."
    "This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$A, AnyTypeOf<[AnyMemRef, AnyTensor]>:$B, AnyTypeOf<[AnyMemRef, AnyTensor]>:$C);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXGlobalAveragePoolOp:ONNX_Op<"GlobalAveragePool", 
    [NoSideEffect]> {
  let summary = "ONNX GlobalAveragePool operation";
  let description = [{
    "GlobalAveragePool consumes an input tensor X and applies average pooling across"
    " the values in the same channel. This is equivalent to AveragePool with kernel size"
    " equal to the spatial dimension of input tensor."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXGlobalLpPoolOp:ONNX_Op<"GlobalLpPool", 
    [NoSideEffect]> {
  let summary = "ONNX GlobalLpPool operation";
  let description = [{
    "GlobalLpPool consumes an input tensor X and applies lp pool pooling across"
    " the values in the same channel. This is equivalent to LpPool with kernel size"
    " equal to the spatial dimension of input tensor."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXGlobalMaxPoolOp:ONNX_Op<"GlobalMaxPool", 
    [NoSideEffect]> {
  let summary = "ONNX GlobalMaxPool operation";
  let description = [{
    "GlobalMaxPool consumes an input tensor X and applies max pooling across"
    " the values in the same channel. This is equivalent to MaxPool with kernel size"
    " equal to the spatial dimension of input tensor."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXGreaterOp:ONNX_Op<"Greater", 
    [NoSideEffect]> {
  let summary = "ONNX Greater operation";
  let description = [{
    "Returns the tensor resulted from performing the `greater` logical operation"
    "elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support)."
    ""
    "This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md)."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$A, AnyTypeOf<[AnyMemRef, AnyTensor]>:$B);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXHardSigmoidOp:ONNX_Op<"HardSigmoid", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let summary = "ONNX HardSigmoid operation";
  let description = [{
    "HardSigmoid takes one input data (Tensor<T>) and produces one output data"
    "(Tensor<T>) where the HardSigmoid function, y = max(0, min(1, alpha * x + beta)),"
    "is applied to the tensor elementwise."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXHardmaxOp:ONNX_Op<"Hardmax", 
    [NoSideEffect]> {
  let summary = "ONNX Hardmax operation";
  let description = [{
    "The operator computes the hardmax (1 for the first maximum value, and 0 for all others) values for each layer in the batch"
    " of the given input."
    ""
    "The input does not need to explicitly be a 2D vector; rather, it will be"
    "coerced into one. For an arbitrary n-dimensional tensor"
    "input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1\}\] and k is"
    "the axis provided, then input will be coerced into a 2-dimensional tensor with"
    "dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1\}\]. For the default"
    "case where axis=1, this means the input tensor will be coerced into a 2D tensor"
    "of dimensions [a_0, a_1 * ... * a_{n-1\}\], where a_0 is often the batch size."
    "In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D."
    "Each of these dimensions must be matched correctly, or else the operator"
    "will throw errors. The output tensor has the same shape"
    "and contains the hardmax values of the corresponding input."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXIdentityOp:ONNX_Op<"Identity", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let hasCanonicalizer = 1;
  let summary = "ONNX Identity operation";
  let description = [{
    "Identity operator"
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXIfOp:ONNX_Op<"If", 
    [NoSideEffect]> {
  let summary = "ONNX If operation";
  let description = [{
    "If conditional"
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$cond);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXInstanceNormalizationOp:ONNX_Op<"InstanceNormalization", 
    [NoSideEffect]> {
  let summary = "ONNX InstanceNormalization operation";
  let description = [{
    "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."
    ""
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input, AnyTypeOf<[AnyMemRef, AnyTensor]>:$scale, AnyTypeOf<[AnyMemRef, AnyTensor]>:$B);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXIsInfOp:ONNX_Op<"IsInf", 
    [NoSideEffect]> {
  let summary = "ONNX IsInf operation";
  let description = [{
    "Map infinity to true and other values to false."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXIsNaNOp:ONNX_Op<"IsNaN", 
    [NoSideEffect]> {
  let summary = "ONNX IsNaN operation";
  let description = [{
    "Returns which elements of the input are NaN."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXLRNOp:ONNX_Op<"LRN", 
    [NoSideEffect]> {
  let summary = "ONNX LRN operation";
  let description = [{
    "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"
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXLSTMOp:ONNX_Op<"LSTM", 
    [NoSideEffect]> {
  let summary = "ONNX LSTM operation";
  let description = [{
    "Computes an one-layer LSTM. This operator is usually supported via some"
    "custom implementation such as CuDNN."
    ""
    "Notations:"
    ""
    "`X` - input tensor"
    ""
    "`i` - input gate"
    ""
    "`o` - output gate"
    ""
    "`f` - forget gate"
    ""
    "`c` - cell gate"
    ""
    "`t` - time step (t-1 means previous time step)"
    ""
    "`W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates"
    ""
    "`R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates"
    ""
    "`Wb[iofc]` - W bias vectors for input, output, forget, and cell gates"
    ""
    "`Rb[iofc]` - R bias vectors for input, output, forget, and cell gates"
    ""
    "`P[iof]`  - P peephole weight vector for input, output, and forget gates"
    ""
    "`WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates"
    ""
    "`RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates"
    ""
    "`WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates"
    ""
    "`RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates"
    ""
    "`PB[iof]`  - P peephole weight vector for backward input, output, and forget gates"
    ""
    "`H` - Hidden state"
    ""
    "`num_directions` - 2 if direction == bidirectional else 1"
    ""
    "Activation functions:"
    ""
    "  Relu(x)                - max(0, x)"
    ""
    "  Tanh(x)                - (1 - e^{-2x})/(1 + e^{-2x})"
    ""
    "  Sigmoid(x)             - 1/(1 + e^{-x})"
    ""
    "  (NOTE: Below are optional)"
    ""
    "  Affine(x)              - alpha*x + beta"
    ""
    "  LeakyRelu(x)           - x if x >= 0 else alpha * x"
    ""
    "  ThresholdedRelu(x)     - x if x >= alpha else 0"
    ""
    "  ScaledTanh(x)          - alpha*Tanh(beta*x)"
    ""
    "  HardSigmoid(x)         - min(max(alpha*x + beta, 0), 1)"
    ""
    "  Elu(x)                 - x if x >= 0 else alpha*(e^x - 1)"
    ""
    "  Softsign(x)            - x/(1 + |x|)"
    ""
    "  Softplus(x)            - log(1 + e^x)"
    ""
    "Equations (Default: f=Sigmoid, g=Tanh, h=Tanh):"
    ""
    "  - it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi)"
    ""
    "  - ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf)"
    ""
    "  - ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc)"
    ""
    "  - Ct = ft (.) Ct-1 + it (.) ct"
    ""
    "  - ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo)"
    ""
    "  - Ht = ot (.) h(Ct)"
    "This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X, AnyTypeOf<[AnyMemRef, AnyTensor]>:$W, AnyTypeOf<[AnyMemRef, AnyTensor]>:$R, AnyTypeOf<[AnyMemRef, AnyTensor]>:$B, AnyTypeOf<[AnyMemRef, AnyTensor]>:$sequence_lens, AnyTypeOf<[AnyMemRef, AnyTensor]>:$initial_h, AnyTypeOf<[AnyMemRef, AnyTensor]>:$initial_c, AnyTypeOf<[AnyMemRef, AnyTensor]>:$P);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>, AnyTypeOf<[AnyMemRef, AnyTensor]>, AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXLeakyReluOp:ONNX_Op<"LeakyRelu", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let summary = "ONNX LeakyRelu operation";
  let description = [{
    "LeakyRelu takes input data (Tensor<T>) and an argument alpha, and produces one"
    "output data (Tensor<T>) where the function `f(x) = alpha * x for x < 0`,"
    "`f(x) = x for x >= 0`, is applied to the data tensor elementwise."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXLessOp:ONNX_Op<"Less", 
    [NoSideEffect]> {
  let summary = "ONNX Less operation";
  let description = [{
    "Returns the tensor resulted from performing the `less` logical operation"
    "elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support)."
    ""
    "This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md)."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$A, AnyTypeOf<[AnyMemRef, AnyTensor]>:$B);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXLogOp:ONNX_Op<"Log", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let summary = "ONNX Log operation";
  let description = [{
    "Calculates the natural log of the given input tensor, element-wise."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXLogSoftmaxOp:ONNX_Op<"LogSoftmax", 
    [NoSideEffect]> {
  let summary = "ONNX LogSoftmax operation";
  let description = [{
    "The operator computes the logsoftmax (log of softmax) values for each layer in the batch"
    " of the given input."
    ""
    "The input does not need to explicitly be a 2D vector; rather, it will be"
    "coerced into one. For an arbitrary n-dimensional tensor"
    "input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1\}\] and k is"
    "the axis provided, then input will be coerced into a 2-dimensional tensor with"
    "dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1\}\]. For the default"
    "case where axis=1, this means the input tensor will be coerced into a 2D tensor"
    "of dimensions [a_0, a_1 * ... * a_{n-1\}\], where a_0 is often the batch size."
    "In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D."
    "Each of these dimensions must be matched correctly, or else the operator"
    "will throw errors. The output tensor has the same shape"
    "and contains the logsoftmax values of the corresponding input."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXLoopOp:ONNX_Op<"Loop", 
    [NoSideEffect]> {
  let summary = "ONNX Loop operation";
  let description = [{
    "Generic Looping construct. This loop has multiple termination conditions:"
    ""
    "1) Trip count. Iteration count specified at runtime. Set by"
    "   specifying the input M. Optional. Set to empty string to omit."
    "   Note that a static trip count (specified at graph construction time) can be"
    "   specified by passing in a constant node for input M."
    "2) Loop termination condition. This is an input to the op that determines"
    "   whether to run the first iteration and also a loop-carried dependency for"
    "   the body graph. The body graph must yield a value for the condition variable,"
    "   whether this input is provided or not."
    ""
    "This table summarizes the operating modes of this operator with equivalent"
    "C-style code:"
    ""
    "    Operator inputs defined as (max_trip_count, condition_var)."
    ""
    "    input ("", ""):"
    "        for (int i=0; ; ++i) {"
    "          cond = ... // Note this value is ignored, but is required in the body"
    "        }"
    ""
    "    input ("", cond) // Note this is analogous to a while loop"
    "        bool cond = ...;"
    "        for (int i=0; cond; ++i) {"
    "          cond = ...;"
    "        }"
    ""
    "    input ("", 1) // Note this is analogous to a do-while loop"
    "        bool cond = true"
    "        for (int i=0; cond; ++i) {"
    "          cond = ...;"
    "        }"
    ""
    "    input (trip_count, "") // Note this is analogous to a for loop"
    "        int trip_count = ..."
    "        for (int i=0; i < trip_count; ++i) {"
    "          cond = ...; // ignored"
    "        }"
    ""
    "    input (trip_count, cond)"
    "        int trip_count = ...;"
    "        bool cond = ...;"
    "        for (int i=0; i < trip_count && cond; ++i) {"
    "          cond = ...;"
    "        }"
    ""
    ""
    "*Sample usage - cond as well as trip count*"
    ""
    "    graph predict-net {"
    "      %a = Constant[value = <Scalar Tensor [3]>]()"
    "      %b = Constant[value = <Scalar Tensor [6]>]()"
    "      %keepgoing = Constant[value = <Scalar Tensor [1]>]()"
    "      %max_trip_count = Constant[value = <Scalar Tensor [10]>]()"
    "      %keepgoing_out, %b_out, %user_defined_vals = Loop[body = <graph body-net>](%max_trip_count, %keepgoing, %b)"
    "      return"
    "    }"
    ""
    "    graph body-net ("
    "      %i[INT32, scalar]           // iteration number"
    "      %keepgoing_in[BOOL, scalar] // incoming loop-termination-condition; not used"
    "      %b_in[INT32, scalar]        // incoming value of loop-carried-dependency b"
    "    ) {"
    "      %my_local = Add(%a, %b_in)"
    "      %b_out = Sub(%a, %b_in) // outgoing value of loop-carried-dependency b"
    "      %keepgoing_out = Greater(%my_local, %b_out) // outgoing loop-termination-condition"
    "      %user_defined_val = Add(%b_in, %b_in) // scan-output value to be accumulated"
    "      return %keepgoing_out, %b_out, %user_defined_val"
    "    }"
    ""
    "*Sample equivalent C code*"
    ""
    "    {"
    "      /* User-defined code (enclosing scope) */"
    "      int a = 3, b = 6;"
    "      bool keepgoing = true; // Analogous to input cond"
    "      /* End user-defined code */"
    ""
    "      /* Implicitly-defined code */"
    "      const int max_trip_count = 10; // Analogous to input M"
    "      int user_defined_vals[]; // Imagine this is resizable"
    "      /* End implicitly-defined code */"
    "      /* initialize loop-carried variables and scan-output variables */"
    "      bool keepgoing_out = keepgoing"
    "      int b_out = b"
    ""
    "      for (int i=0; i < max_trip_count && keepgoing_out; ++i) {"
    "        /* Implicitly-defined code: bind actual parameter values"
    "           to formal parameter variables of loop-body */"
    "        bool keepgoing_in = keepgoing_out; "
    "        bool b_in = b_out;"
    ""
    "        /* User-defined code (loop body) */"
    "        int my_local = a + b_in; // Reading value "a" from the enclosing scope is fine"
    "        b_out = a - b_in;"
    "        keepgoing_out = my_local > b_out; "
    "        user_defined_val = b_in + b_in; // b_in and b_out are different variables"
    "        /* End user-defined code */"
    ""
    "        /* Implicitly defined-code */"
    "        user_defined_vals[i] = user_defined_val // accumulate scan-output values"
    "      }"
    "      // int t = my_local; // Can't do this. my_local is not accessible here."
    ""
    "      // The values below are bound to the output variables of the loop and therefore accessible"
    "      // b_out; user_defined_vals; keepgoing_out;"
    "    }"
    ""
    "There are several things of note in this code snippet:"
    ""
    "1) Values from the enclosing scope (i.e. variable "a" here) are in scope and can"
    "   be referenced in the inputs of the loop."
    "2) Any values computed in the loop body that needs to be used in a subsequent"
    "   iteration or after the loop are modelled using a pair of variables in the loop-body,"
    "   consisting of an input variable (eg., b_in) and an output variable (eg., b_out)."
    "   These are referred to as loop-carried dependences. The loop operation node"
    "   supplies the input value of the input variable for the first iteration, and"
    "   returns the output value of the output variable produced by the final"
    "   iteration."
    "3) Scan_output variables are used to implicitly concatenate values computed across"
    "   all the iterations. In the above example, the value of user_defined_val computed"
    "   over all iterations are concatenated and returned as the value of user_defined_vals"
    "   after the loop."
    "4) Values created in the body cannot be accessed in the enclosing scope,"
    "   except using the mechanism described above."
    ""
    "Note that the semantics of this op support "diagonal" or "wavefront" execution."
    "(See Step 3 here for an example:"
    "https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/)."
    "Frontends should emit multi-layer RNNs as a series of While operators (with"
    "time being the inner looping dimension), with each successive layer consuming"
    "the scan_outputs from the previous layer, possibly going through several"
    "point-wise operators (e.g. dropout, residual connections, linear layer)."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$M, AnyTypeOf<[AnyMemRef, AnyTensor]>:$cond, AnyTypeOf<[AnyMemRef, AnyTensor]>:$v_initial);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXLpNormalizationOp:ONNX_Op<"LpNormalization", 
    [NoSideEffect]> {
  let summary = "ONNX LpNormalization operation";
  let description = [{
    "Given a matrix, apply Lp-normalization along the provided axis."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXLpPoolOp:ONNX_Op<"LpPool", 
    [NoSideEffect]> {
  let summary = "ONNX LpPool operation";
  let description = [{
    "LpPool 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."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXMatMulOp:ONNX_Op<"MatMul", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let summary = "ONNX MatMul operation";
  let description = [{
    "Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html"
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$A, AnyTypeOf<[AnyMemRef, AnyTensor]>:$B);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXMatMulIntegerOp:ONNX_Op<"MatMulInteger", 
    [NoSideEffect]> {
  let summary = "ONNX MatMulInteger operation";
  let description = [{
    "Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html."
    "The production MUST never overflow. The accumulation may overflow if and only if in 32 bits."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$A, AnyTypeOf<[AnyMemRef, AnyTensor]>:$B, AnyTypeOf<[AnyMemRef, AnyTensor]>:$a_zero_point, AnyTypeOf<[AnyMemRef, AnyTensor]>:$b_zero_point);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXMaxOp:ONNX_Op<"Max", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let summary = "ONNX Max operation";
  let description = [{
    "Element-wise max of each of the input tensors (with Numpy-style broadcasting support)."
    "All inputs and outputs must have the same data type."
    "This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md)."
  }];
  let arguments = (ins Variadic<AnyTypeOf<[AnyMemRef, AnyTensor]>>:$data_0);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXMaxPoolOp:ONNX_Op<"MaxPool", 
    [NoSideEffect]> {
  let summary = "ONNX MaxPool operation";
  let description = [{
    "MaxPool consumes an input tensor X and applies max pooling across"
    " the tensor according to kernel sizes, stride sizes, and pad lengths."
    " max pooling consisting of computing the max 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] - 1) * dilations[i] + 1)) / strides_spatial_shape[i] + 1)"
    " ```"
    " or"
    " ```"
    " output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / 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] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 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] - 1) * dilations[i] + 1) - input_spatial_shape[i]"
    " ```"
    " The output of each pooling window is maximum number of elements exclude pad."
    " "
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>, AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXMaxRoiPoolOp:ONNX_Op<"MaxRoiPool", 
    [NoSideEffect]> {
  let summary = "ONNX MaxRoiPool operation";
  let description = [{
    "ROI max pool consumes an input tensor X and region of interests (RoIs) to"
    " apply max pooling across each RoI, to produce output 4-D tensor of shape"
    " (num_rois, channels, pooled_shape[0], pooled_shape[1])."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X, AnyTypeOf<[AnyMemRef, AnyTensor]>:$rois);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXMaxUnpoolOp:ONNX_Op<"MaxUnpool", 
    [NoSideEffect]> {
  let summary = "ONNX MaxUnpool operation";
  let description = [{
    "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."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X, AnyTypeOf<[AnyMemRef, AnyTensor]>:$I, AnyTypeOf<[AnyMemRef, AnyTensor]>:$output_shape);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXMeanOp:ONNX_Op<"Mean", 
    [NoSideEffect]> {
  let summary = "ONNX Mean operation";
  let description = [{
    "Element-wise mean of each of the input tensors (with Numpy-style broadcasting support)."
    "All inputs and outputs must have the same data type."
    "This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md)."
  }];
  let arguments = (ins Variadic<AnyTypeOf<[AnyMemRef, AnyTensor]>>:$data_0);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXMeanVarianceNormalizationOp:ONNX_Op<"MeanVarianceNormalization", 
    [NoSideEffect]> {
  let summary = "ONNX MeanVarianceNormalization operation";
  let description = [{
    "A MeanVarianceNormalization Function: Perform mean variance normalization"
    "      on the input tensor X using formula: <br/> ``` (X-EX)/sqrt(E(X-EX)^2) ```"
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXMinOp:ONNX_Op<"Min", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let summary = "ONNX Min operation";
  let description = [{
    "Element-wise min of each of the input tensors (with Numpy-style broadcasting support)."
    "All inputs and outputs must have the same data type."
    "This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md)."
  }];
  let arguments = (ins Variadic<AnyTypeOf<[AnyMemRef, AnyTensor]>>:$data_0);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXModOp:ONNX_Op<"Mod", 
    [NoSideEffect]> {
  let summary = "ONNX Mod operation";
  let description = [{
    "Performs element-wise binary modulus (with Numpy-style broadcasting support). "
    "    The sign of the remainder is the same as that of the Divisor."
    "  "
    "    Mod operator can also behave like C fmod() or numpy.fmod. In this case, the sign of the remainder however, will be the same as the Dividend "
    "    (in contrast to integer mod). To force a behavior like numpy.fmod() an 'fmod' Attribute is provided."
    "    This attribute is set to 0 by default causing the behavior to be like integer mod. "
    "    Setting this attribute to 1 causes the remainder to be calculated similar to that of numpy.fmod()."
    ""
    "    If the input type is floating point, then `fmod` attribute must be set to 1."
    "  "
    "    In case of dividend being zero, the results will be platform dependent."
    ""
    "  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md)."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$A, AnyTypeOf<[AnyMemRef, AnyTensor]>:$B);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXMulOp:ONNX_Op<"Mul", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let summary = "ONNX Mul operation";
  let description = [{
    "Performs element-wise binary multiplication (with Numpy-style broadcasting support)."
    ""
    "This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md)."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$A, AnyTypeOf<[AnyMemRef, AnyTensor]>:$B);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXMultinomialOp:ONNX_Op<"Multinomial", 
    [NoSideEffect]> {
  let summary = "ONNX Multinomial operation";
  let description = [{
    "Generate a tensor of samples from a multinomial distribution according to the probabilities"
    "of each of the possible outcomes."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXNegOp:ONNX_Op<"Neg", 
    [NoSideEffect]> {
  let summary = "ONNX Neg operation";
  let description = [{
    "Neg takes one input data (Tensor<T>) and produces one output data"
    "(Tensor<T>) where each element flipped sign, y = -x, is applied to"
    "the tensor elementwise."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXNonMaxSuppressionOp:ONNX_Op<"NonMaxSuppression", 
    [NoSideEffect]> {
  let summary = "ONNX NonMaxSuppression operation";
  let description = [{
    "Filter out boxes that have high intersection-over-union (IOU) overlap with previously selected boxes."
    "Bounding boxes with score less than score_threshold are removed. Bounding box format is indicated by attribute center_point_box."
    "Note that this algorithm is agnostic to where the origin is in the coordinate system and more generally is invariant to"
    "orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system"
    "result in the same boxes being selected by the algorithm."
    "The selected_indices output is a set of integers indexing into the input collection of bounding boxes representing the selected boxes."
    "The bounding box coordinates corresponding to the selected indices can then be obtained using the Gather or GatherND operation."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$boxes, AnyTypeOf<[AnyMemRef, AnyTensor]>:$scores, AnyTypeOf<[AnyMemRef, AnyTensor]>:$max_output_boxes_per_class, AnyTypeOf<[AnyMemRef, AnyTensor]>:$iou_threshold, AnyTypeOf<[AnyMemRef, AnyTensor]>:$score_threshold);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXNonZeroOp:ONNX_Op<"NonZero", 
    [NoSideEffect]> {
  let summary = "ONNX NonZero operation";
  let description = [{
    "Returns the indices of the elements that are non-zero"
    "    (in row-major order - by dimension)."
    "    NonZero behaves similar to numpy.nonzero:"
    "    https://docs.scipy.org/doc/numpy/reference/generated/numpy.nonzero.html"
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXNotOp:ONNX_Op<"Not", 
    [NoSideEffect]> {
  let summary = "ONNX Not operation";
  let description = [{
    "Returns the negation of the input tensor element-wise."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXOneHotOp:ONNX_Op<"OneHot", 
    [NoSideEffect]> {
  let summary = "ONNX OneHot operation";
  let description = [{
    "Produces a one-hot tensor based on inputs."
    "    The locations represented by the index values in the 'indices' input tensor will have 'on_value'"
    "    and the other locations will have 'off_value' in the output tensor, where 'on_value' and 'off_value'"
    "    are specified as part of required input argument 'values', which is a two-element tensor of format"
    "    [off_value, on_value]. The rank of the output tensor will be one greater than the rank of the"
    "    input tensor. The additional dimension is for one-hot representation. The additional dimension will"
    "    be inserted at the position specified by 'axis'. If 'axis' is not specified then then additional"
    "    dimension will be inserted as the innermost dimension, i.e. axis=-1. The size of the additional"
    "    dimension is specified by required scalar input 'depth'. The type of the output tensor is the same"
    "    as the type of the 'values' input. Any entries in the 'indices' input tensor with values outside"
    "    the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the"
    "    output tensor."
    ""
    "    when axis = 0:"
    "    output[input[i, j, k], i, j, k] = 1 for all i, j, k and 0 otherwise."
    ""
    "    when axis = -1:"
    "    output[i, j, k, input[i, j, k]] = 1 for all i, j, k and 0 otherwise."
    ""
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$indices, AnyTypeOf<[AnyMemRef, AnyTensor]>:$depth, AnyTypeOf<[AnyMemRef, AnyTensor]>:$values);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXOrOp:ONNX_Op<"Or", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let summary = "ONNX Or operation";
  let description = [{
    "Returns the tensor resulted from performing the `or` logical operation"
    "elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support)."
    ""
    "This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md)."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$A, AnyTypeOf<[AnyMemRef, AnyTensor]>:$B);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXPReluOp:ONNX_Op<"PRelu", 
    [NoSideEffect]> {
  let summary = "ONNX PRelu operation";
  let description = [{
    "PRelu takes input data (Tensor<T>) and slope tensor as input, and produces one"
    "output data (Tensor<T>) where the function `f(x) = slope * x for x < 0`,"
    "`f(x) = x for x >= 0`., is applied to the data tensor elementwise."
    "This operator supports **unidirectional broadcasting** (tensor slope should be unidirectional broadcastable to input tensor X); for more details please check [the doc](Broadcasting.md)."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X, AnyTypeOf<[AnyMemRef, AnyTensor]>:$slope);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXPadOp:ONNX_Op<"Pad", 
    [NoSideEffect]> {
  let summary = "ONNX Pad operation";
  let description = [{
    "Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`, "
    "a padded tensor (`output`) is generated."
    ""
    "The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`):"
    ""
    "1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0)"
    ""
    "2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis"
    ""
    "3) `edge` - pads with the edge values of array"
    ""
    ""
    "Example 1 (`constant` mode):"
    "  Insert 0 pads to the beginning of the second dimension."
    ""
    "  data = "
    "  ["
    "      [1.0, 1.2],"
    "      [2.3, 3.4],"
    "      [4.5, 5.7],"
    "  ] "
    ""
    "  pads = [0, 2, 0, 0]"
    ""
    "  mode = 'constant'"
    ""
    "  constant_value = 0.0"
    ""
    "  output = "
    "  ["
    "      ["
    "          [0.0, 0.0, 1.0, 1.2],"
    "          [0.0, 0.0, 2.3, 3.4],"
    "          [0.0, 0.0, 4.5, 5.7],"
    "      ],"
    "  ]"
    ""
    ""
    "Example 2 (`reflect` mode):"
    "  data = "
    "  ["
    "      [1.0, 1.2],"
    "      [2.3, 3.4],"
    "      [4.5, 5.7],"
    "  ] "
    ""
    "  pads = [0, 2, 0, 0]"
    ""
    "  mode = 'reflect'"
    ""
    "  output = "
    "  ["
    "      ["
    "          [1.0, 1.2, 1.0, 1.2],"
    "          [2.3, 3.4, 2.3, 3.4],"
    "          [4.5, 5.7, 4.5, 5.7],"
    "      ],"
    "  ]"
    ""
    ""
    "Example 3 (`edge` mode):"
    "  data = "
    "  ["
    "      [1.0, 1.2],"
    "      [2.3, 3.4],"
    "      [4.5, 5.7],"
    "  ] "
    ""
    "  pads = [0, 2, 0, 0]"
    ""
    "  mode = 'edge'"
    ""
    "  output = "
    "  ["
    "      ["
    "          [1.0, 1.0, 1.0, 1.2],"
    "          [2.3, 2.3, 2.3, 3.4],"
    "          [4.5, 4.5, 4.5, 5.7],"
    "      ],"
    "  ]"
    ""
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data, AnyTypeOf<[AnyMemRef, AnyTensor]>:$pads, AnyTypeOf<[AnyMemRef, AnyTensor]>:$constant_value);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXPowOp:ONNX_Op<"Pow", 
    [NoSideEffect]> {
  let summary = "ONNX Pow operation";
  let description = [{
    "Pow takes input data (Tensor<T>) and exponent Tensor, and"
    "produces one output data (Tensor<T>) where the function `f(x) = x^exponent`,"
    "is applied to the data tensor elementwise."
    "This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md)."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X, AnyTypeOf<[AnyMemRef, AnyTensor]>:$Y);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXQLinearConvOp:ONNX_Op<"QLinearConv", 
    [NoSideEffect]> {
  let summary = "ONNX QLinearConv operation";
  let description = [{
    "The convolution operator consumes a quantized input tensor, its scale and zero point,"
    "a quantized filter, its scale and zero point, and output's scale and zero point,"
    "and computes the quantized output. Each scale and zero-point pair must have same shape."
    "It means they must be either scalars (per tensor) or 1-D tensors (per output channel)."
    "Each input or output and its related zero point must have same type."
    "When bias is present it must be quantized using scale = input scale * weight scale and "
    "zero point as 0."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$x, AnyTypeOf<[AnyMemRef, AnyTensor]>:$x_scale, AnyTypeOf<[AnyMemRef, AnyTensor]>:$x_zero_point, AnyTypeOf<[AnyMemRef, AnyTensor]>:$w, AnyTypeOf<[AnyMemRef, AnyTensor]>:$w_scale, AnyTypeOf<[AnyMemRef, AnyTensor]>:$w_zero_point, AnyTypeOf<[AnyMemRef, AnyTensor]>:$y_scale, AnyTypeOf<[AnyMemRef, AnyTensor]>:$y_zero_point, AnyTypeOf<[AnyMemRef, AnyTensor]>:$B);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXQLinearMatMulOp:ONNX_Op<"QLinearMatMul", 
    [NoSideEffect]> {
  let summary = "ONNX QLinearMatMul operation";
  let description = [{
    "Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html."
    "It consumes two quantized input tensors, their scales and zero points, scale and zero point of output, and computes the quantized output."
    "The quantization formula is y = saturate((x / y_scale) + y_zero_point). For (x / y_scale), it is rounding to nearest ties to even."
    "Refer to https://en.wikipedia.org/wiki/Rounding for details. Scale and zero point must have same shape."
    "They must be either scalar (per tensor) or 1-D tensor (per row for 'a' and per column for 'b'). If scale and zero point are 1-D tensor,"
    "the number of elements of scale and zero point tensor of input 'a' and output 'y' should be equal to the number of rows of input 'a',"
    "and the number of elements of scale and zero point tensor of input 'b' should be equal to the number of columns of input 'b'."
    "Production must never overflow, and accumulation may overflow if and only if in 32 bits."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$a, AnyTypeOf<[AnyMemRef, AnyTensor]>:$a_scale, AnyTypeOf<[AnyMemRef, AnyTensor]>:$a_zero_point, AnyTypeOf<[AnyMemRef, AnyTensor]>:$b, AnyTypeOf<[AnyMemRef, AnyTensor]>:$b_scale, AnyTypeOf<[AnyMemRef, AnyTensor]>:$b_zero_point, AnyTypeOf<[AnyMemRef, AnyTensor]>:$y_scale, AnyTypeOf<[AnyMemRef, AnyTensor]>:$y_zero_point);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXQuantizeLinearOp:ONNX_Op<"QuantizeLinear", 
    [NoSideEffect]> {
  let summary = "ONNX QuantizeLinear operation";
  let description = [{
    "The linear per-tensor/layer quantization operator. It consumes a high precision tensor, a scale, a zero point to compute the low precision / quantized tensor."
    "The quantization formula is y = saturate ((x / y_scale) + y_zero_point). For saturation, it saturates to [0, 255] if it's uint8, or [-128, 127] if it's int8."
    "For (x / y_scale), it's rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details. 'y_zero_point' and 'y' must have same type."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$x, AnyTypeOf<[AnyMemRef, AnyTensor]>:$y_scale, AnyTypeOf<[AnyMemRef, AnyTensor]>:$y_zero_point);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXRNNOp:ONNX_Op<"RNN", 
    [NoSideEffect]> {
  let summary = "ONNX RNN operation";
  let description = [{
    "Computes an one-layer simple RNN. This operator is usually supported"
    "via some custom implementation such as CuDNN."
    ""
    "Notations:"
    ""
    "`X` - input tensor"
    ""
    "`i` - input gate"
    ""
    "`t` - time step (t-1 means previous time step)"
    ""
    "`Wi` - W parameter weight matrix for input gate"
    ""
    "`Ri` - R recurrence weight matrix for input gate"
    ""
    "`Wbi` - W parameter bias vector for input gate"
    ""
    "`Rbi` - R parameter bias vector for input gate"
    ""
    "`WBi` - W parameter weight matrix for backward input gate"
    ""
    "`RBi` - R recurrence weight matrix for backward input gate"
    ""
    "`WBbi` - WR bias vectors for backward input gate"
    ""
    "`RBbi` - RR bias vectors for backward input gate"
    ""
    "`H` - Hidden state"
    ""
    "`num_directions` - 2 if direction == bidirectional else 1"
    ""
    "Activation functions:"
    ""
    "  Relu(x)                - max(0, x)"
    ""
    "  Tanh(x)                - (1 - e^{-2x})/(1 + e^{-2x})"
    ""
    "  Sigmoid(x)             - 1/(1 + e^{-x})"
    ""
    "  (NOTE: Below are optional)"
    ""
    "  Affine(x)              - alpha*x + beta"
    ""
    "  LeakyRelu(x)           - x if x >= 0 else alpha * x"
    ""
    "  ThresholdedRelu(x)     - x if x >= alpha else 0"
    ""
    "  ScaledTanh(x)          - alpha*Tanh(beta*x)"
    ""
    "  HardSigmoid(x)         - min(max(alpha*x + beta, 0), 1)"
    ""
    "  Elu(x)                 - x if x >= 0 else alpha*(e^x - 1)"
    ""
    "  Softsign(x)            - x/(1 + |x|)"
    ""
    "  Softplus(x)            - log(1 + e^x)"
    ""
    "Equations (Default: f=Tanh):"
    ""
    "  - Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi)"
    "This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X, AnyTypeOf<[AnyMemRef, AnyTensor]>:$W, AnyTypeOf<[AnyMemRef, AnyTensor]>:$R, AnyTypeOf<[AnyMemRef, AnyTensor]>:$B, AnyTypeOf<[AnyMemRef, AnyTensor]>:$sequence_lens, AnyTypeOf<[AnyMemRef, AnyTensor]>:$initial_h);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>, AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXRandomNormalOp:ONNX_Op<"RandomNormal", 
    [NoSideEffect]> {
  let summary = "ONNX RandomNormal operation";
  let description = [{
    "Generate a tensor with random values drawn from a normal distribution. The shape"
    "of the tensor is specified by the `shape` argument and the parameter of the normal distribution"
    "specified by `mean` and `scale`."
    ""
    "The data type is specified by the 'dtype' argument. The 'dtype' argument must"
    "be one of the data types specified in the 'DataType' enum field in the"
    "TensorProto message."
  }];
  let arguments = (ins );
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXRandomNormalLikeOp:ONNX_Op<"RandomNormalLike", 
    [NoSideEffect]> {
  let summary = "ONNX RandomNormalLike operation";
  let description = [{
    "Generate a tensor with random values drawn from a normal distribution."
    "The shape of the output tensor is copied from the shape of the input tensor,"
    "and the parameters of the normal distribution are specified by `mean` and `scale`."
    ""
    "The data type is specified by the 'dtype' argument, or copied from the input tensor if not provided."
    "The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the"
    "TensorProto message, and be valid as an output type."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXRandomUniformOp:ONNX_Op<"RandomUniform", 
    [NoSideEffect]> {
  let summary = "ONNX RandomUniform operation";
  let description = [{
    "Generate a tensor with random values drawn from a uniform distribution. The shape"
    "of the tensor is specified by the `shape` argument and the range by `low` and `high`."
    ""
    "The data type is specified by the 'dtype' argument. The 'dtype' argument must"
    "be one of the data types specified in the 'DataType' enum field in the"
    "TensorProto message."
  }];
  let arguments = (ins );
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXRandomUniformLikeOp:ONNX_Op<"RandomUniformLike", 
    [NoSideEffect]> {
  let summary = "ONNX RandomUniformLike operation";
  let description = [{
    "Generate a tensor with random values drawn from a uniform distribution."
    "The shape of the output tensor is copied from the shape of the input tensor,"
    "and the parameters of the uniform distribution are specified by `low` and `high`."
    ""
    "The data type is specified by the 'dtype' argument, or copied from the input tensor if not provided."
    "The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the"
    "TensorProto message and be valid as an output type."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXRangeOp:ONNX_Op<"Range", 
    [NoSideEffect]> {
  let summary = "ONNX Range operation";
  let description = [{
    "Generate a tensor containing a sequence of numbers that begin at `start` and extends by increments of `delta` "
    "up to `limit` (exclusive)."
    ""
    "The number of elements in the output of range is computed as below-"
    ""
    "`number_of_elements = max( ceil( (limit - start) / delta ) , 0 )`"
    ""
    "The pseudocode determining the contents of the output is shown below-"
    ""
    "`for(int i=0; i<number_of_elements; ++i)`"
    ""
    "`{`"
    "   "
    "`    output[i] =  start + (i * delta);  ` "
    ""
    "`}`	"
    ""
    "`Example 1`"
    "Inputs: start = 3, limit = 9, delta = 3"
    "Output: [3, 6]"
    ""
    "`Example 2`"
    "Inputs: start = 10, limit = 4, delta = -2"
    "Output: [10, 8, 6]"
    ""
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$start, AnyTypeOf<[AnyMemRef, AnyTensor]>:$limit, AnyTypeOf<[AnyMemRef, AnyTensor]>:$delta);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXReciprocalOp:ONNX_Op<"Reciprocal", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let summary = "ONNX Reciprocal operation";
  let description = [{
    "Reciprocal takes one input data (Tensor<T>) and produces one output data"
    "(Tensor<T>) where the reciprocal is, y = 1/x, is applied to"
    "the tensor elementwise."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXReduceL1Op:ONNX_Op<"ReduceL1", 
    [NoSideEffect]> {
  let summary = "ONNX ReduceL1 operation";
  let description = [{
    "Computes the L1 norm of the input tensor's element along the provided axes. The resulted"
    "tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then"
    "the resulted tensor have the reduced dimension pruned."
    ""
    "The above behavior is similar to numpy, with the exception that numpy default keepdims to"
    "False instead of True."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXReduceL2Op:ONNX_Op<"ReduceL2", 
    [NoSideEffect]> {
  let summary = "ONNX ReduceL2 operation";
  let description = [{
    "Computes the L2 norm of the input tensor's element along the provided axes. The resulted"
    "tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then"
    "the resulted tensor have the reduced dimension pruned."
    ""
    "The above behavior is similar to numpy, with the exception that numpy default keepdims to"
    "False instead of True."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXReduceLogSumOp:ONNX_Op<"ReduceLogSum", 
    [NoSideEffect]> {
  let summary = "ONNX ReduceLogSum operation";
  let description = [{
    "Computes the log sum of the input tensor's element along the provided axes. The resulted"
    "tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then"
    "the resulted tensor have the reduced dimension pruned."
    ""
    "The above behavior is similar to numpy, with the exception that numpy default keepdims to"
    "False instead of True."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXReduceLogSumExpOp:ONNX_Op<"ReduceLogSumExp", 
    [NoSideEffect]> {
  let summary = "ONNX ReduceLogSumExp operation";
  let description = [{
    "Computes the log sum exponent of the input tensor's element along the provided axes. The resulted"
    "tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then"
    "the resulted tensor have the reduced dimension pruned."
    ""
    "The above behavior is similar to numpy, with the exception that numpy default keepdims to"
    "False instead of True."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXReduceMaxOp:ONNX_Op<"ReduceMax", 
    [NoSideEffect]> {
  let summary = "ONNX ReduceMax operation";
  let description = [{
    "Computes the max of the input tensor's element along the provided axes. The resulted"
    "tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then"
    "the resulted tensor have the reduced dimension pruned."
    ""
    "The above behavior is similar to numpy, with the exception that numpy default keepdims to"
    "False instead of True."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXReduceMeanOp:ONNX_Op<"ReduceMean", 
    [NoSideEffect]> {
  let summary = "ONNX ReduceMean operation";
  let description = [{
    "Computes the mean of the input tensor's element along the provided axes. The resulted"
    "tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then"
    "the resulted tensor have the reduced dimension pruned."
    ""
    "The above behavior is similar to numpy, with the exception that numpy default keepdims to"
    "False instead of True."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXReduceMinOp:ONNX_Op<"ReduceMin", 
    [NoSideEffect]> {
  let summary = "ONNX ReduceMin operation";
  let description = [{
    "Computes the min of the input tensor's element along the provided axes. The resulted"
    "tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then"
    "the resulted tensor have the reduced dimension pruned."
    ""
    "The above behavior is similar to numpy, with the exception that numpy default keepdims to"
    "False instead of True."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXReduceProdOp:ONNX_Op<"ReduceProd", 
    [NoSideEffect]> {
  let summary = "ONNX ReduceProd operation";
  let description = [{
    "Computes the product of the input tensor's element along the provided axes. The resulted"
    "tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then"
    "the resulted tensor have the reduced dimension pruned."
    ""
    "The above behavior is similar to numpy, with the exception that numpy default keepdims to"
    "False instead of True."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXReduceSumOp:ONNX_Op<"ReduceSum", 
    [NoSideEffect]> {
  let summary = "ONNX ReduceSum operation";
  let description = [{
    "Computes the sum of the input tensor's element along the provided axes. The resulted"
    "tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then"
    "the resulted tensor have the reduced dimension pruned."
    ""
    "The above behavior is similar to numpy, with the exception that numpy default keepdims to"
    "False instead of True."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXReduceSumSquareOp:ONNX_Op<"ReduceSumSquare", 
    [NoSideEffect]> {
  let summary = "ONNX ReduceSumSquare operation";
  let description = [{
    "Computes the sum square of the input tensor's element along the provided axes. The resulted"
    "tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then"
    "the resulted tensor have the reduced dimension pruned."
    ""
    "The above behavior is similar to numpy, with the exception that numpy default keepdims to"
    "False instead of True."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXReluOp:ONNX_Op<"Relu", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let summary = "ONNX Relu operation";
  let description = [{
    "Relu takes one input data (Tensor<T>) and produces one output data"
    "(Tensor<T>) where the rectified linear function, y = max(0, x), is applied to"
    "the tensor elementwise."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXReshapeOp:ONNX_Op<"Reshape", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let summary = "ONNX Reshape operation";
  let description = [{
    "Reshape the input tensor similar to numpy.reshape."
    "First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor."
    "At most one dimension of the new shape can be -1. In this case, the value is"
    "inferred from the size of the tensor and the remaining dimensions. A dimension"
    "could also be 0, in which case the actual dimension value is unchanged (i.e. taken"
    "from the input tensor)."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data, AnyTypeOf<[AnyMemRef, AnyTensor]>:$shape);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXResizeOp:ONNX_Op<"Resize", 
    [NoSideEffect]> {
  let summary = "ONNX Resize operation";
  let description = [{
    "Resize the input tensor. In general, it calculates every value in the output tensor as a weighted average of neighborhood (a.k.a. sampling locations) in the input tensor."
    "Each dimension value of the output tensor is:"
    "  output_dimension = floor(input_dimension * (roi_end - roi_start) * scale) if input \"sizes\" is not specified."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X, AnyTypeOf<[AnyMemRef, AnyTensor]>:$roi, AnyTypeOf<[AnyMemRef, AnyTensor]>:$scales, AnyTypeOf<[AnyMemRef, AnyTensor]>:$sizes);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXReverseSequenceOp:ONNX_Op<"ReverseSequence", 
    [NoSideEffect]> {
  let summary = "ONNX ReverseSequence operation";
  let description = [{
    "Reverse batch of sequences having different lengths specified by `sequence_lens`."
    ""
    "For each slice i iterating on batch axis, the operator reverses the first sequence_lens[i] elements on time axis,"
    "and copies elements whose index's beyond sequence_lens[i] to the output. So the output slice i contains reversed"
    "sequences on the first sequence_lens[i] elements, then have original values copied for the other elements."
    ""
    "Example 1:"
    "  input = [[0.0, 4.0, 8.0,  12.0],"
    "           [1.0, 5.0, 9.0,  13.0],"
    "           [2.0, 6.0, 10.0, 14.0],"
    "           [3.0, 7.0, 11.0, 15.0]]"
    "  sequence_lens = [4, 3, 2, 1]"
    "  time_axis = 0"
    "  batch_axis = 1"
    ""
    "  output = [[3.0, 6.0, 9.0,  12.0],"
    "            [2.0, 5.0, 8.0,  13.0],"
    "            [1.0, 4.0, 10.0, 14.0],"
    "            [0.0, 7.0, 11.0, 15.0]]"
    ""
    "Example 2:"
    "  input = [[0.0,  1.0,  2.0,  3.0 ],"
    "           [4.0,  5.0,  6.0,  7.0 ],"
    "           [8.0,  9.0,  10.0, 11.0],"
    "           [12.0, 13.0, 14.0, 15.0]]"
    "  sequence_lens = [1, 2, 3, 4]"
    "  time_axis = 1"
    "  batch_axis = 0"
    ""
    "  output = [[0.0,  1.0,  2.0,  3.0 ],"
    "            [5.0,  4.0,  6.0,  7.0 ],"
    "            [10.0, 9.0,  8.0,  11.0],"
    "            [15.0, 14.0, 13.0, 12.0]]"
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input, AnyTypeOf<[AnyMemRef, AnyTensor]>:$sequence_lens);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXRoiAlignOp:ONNX_Op<"RoiAlign", 
    [NoSideEffect]> {
  let summary = "ONNX RoiAlign operation";
  let description = [{
    "Region of Interest (RoI) align operation described in the"
    "[Mask R-CNN paper](https://arxiv.org/abs/1703.06870)."
    "RoiAlign consumes an input tensor X and region of interests (rois)"
    "to apply pooling across each RoI; it produces a 4-D tensor of shape"
    "(num_rois, C, output_height, output_width)."
    ""
    "RoiAlign is proposed to avoid the misalignment by removing"
    "quantizations while converting from original image into feature"
    "map and from feature map into RoI feature; in each ROI bin,"
    "the value of the sampled locations are computed directly"
    "through bilinear interpolation."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X, AnyTypeOf<[AnyMemRef, AnyTensor]>:$rois, AnyTypeOf<[AnyMemRef, AnyTensor]>:$batch_indices);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXRoundOp:ONNX_Op<"Round", 
    [NoSideEffect]> {
  let summary = "ONNX Round operation";
  let description = [{
    "Round takes one input Tensor and rounds the values, element-wise, meaning"
    "it finds the nearest integer for each value."
    "In case of halfs, the rule is to round them to the nearest even integer."
    "The output tensor has the same shape and type as the input."
    ""
    "Examples:"
    "```"
    "round([0.9]) = [1.0]"
    "round([2.5]) = [2.0]"
    "round([2.3]) = [2.0]"
    "round([1.5]) = [2.0]"
    "round([-4.5]) = [-4.0]"
    "```"
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXScanOp:ONNX_Op<"Scan", 
    [NoSideEffect]> {
  let summary = "ONNX Scan operation";
  let description = [{
    "Scan can be used to iterate over one or more scan_input tensors,"
    "constructing zero or more scan_output tensors. It combines ideas from general recurrences,"
    "functional programming constructs such as scan, fold, map, and zip and is intended to enable"
    "generalizations of RNN-like constructs for sequence-to-sequence processing."
    "Other tensors (referred to as state_variables here) can be used to carry a state"
    "when iterating from one element to another (similar to hidden-state in RNNs, also referred"
    "to as loop-carried dependences in the context of loops)."
    "Many common usages involve a single scan_input tensor (where functionality"
    "similar to scan, fold and map can be obtained). When more than one scan_input is used,"
    "a behavior similar to zip is obtained."
    ""
    "The attribute body must be a graph, specifying the computation to be performed in"
    "every iteration. It takes as input the current values of the state_variables and"
    "the current iterated element of the scan_inputs. It must return the (updated) values"
    "of the state_variables and zero or more scan_output_element tensors. The values of the"
    "scan_output_element tensors are concatenated over all the iterations to produce the"
    "scan_output values of the scan construct (similar to the concatenated intermediate"
    "hidden-state values of RNN-like constructs). All the output tensors (state_variables as"
    "well as scan_output_element tensors) are required to have the same shape in each iteration"
    "of the loop (a restriction imposed to enable efficient memory allocation)."
    ""
    "Note that the iterated element passed to the body subgraph does not have a sequence"
    "axis. It will have a rank one less than the rank of the corresponding scan_input."
    ""
    "The scan operation returns the final values of the state_variables as well as the"
    "scan_outputs."
    ""
    "The optional attribute scan_input_directions specifies the direction (forward or backward)"
    "for each scan input. If this attribute is omitted, all sequences are scanned in the forward"
    "direction. A bidirectional scan may be performed by specifying the same tensor input twice"
    "in the scan_inputs, once with a forward direction, and once with a backward direction."
    ""
    "The scan_output of the operation is produced by concatenating the scan_output_element"
    "values produced by the body in each iteration.  The optional attribute scan_output_directions"
    "specifies the direction in which scan_output is constructed (by appending or prepending the"
    "scan_output_element to scan_output in each iteration) for each scan_output. If this attribute"
    "is omitted, the scan_output_element is appended to the scan_output in each iteration."
    ""
    "The optional attribute scan_input_axes specifies the axis to be scanned for each scan_input."
    "If omitted, every scan_input will be scanned in axis 0. For example, if axis 0 is the"
    "batch axis and axis 1 is the time axis (to be scanned), specify an axis value of 1."
    "Note that scanning a non-zero axis may be less efficient than scanning axis zero."
    ""
    "The optional attribute scan_output_axes specifies the axis along which the scan_outputs"
    "are accumulated for each scan_output. For example, if axis 1 is the time axis (to be"
    "scanned) for both inputs and outputs, specify a scan_input axis and scan_output axis"
    "value of 1."
    ""
    "Note that because of the ONNX restriction that only the last parameter of an operator can"
    "be variadic, the initial-states and scan-inputs are listed together as one input parameter."
    "Similarly, the final-states and scan-outputs are listed together as one output parameter."
    "The attribute num_scan_inputs indicates the number M of scan-inputs."
    ""
    "The behavior of"
    ""
    "    Scan <"
    "        num_scan_inputs = m,"
    "        body = loop-body,"
    "        scan_input_axes = [axis_1, ..., axis_m]"
    "    > (init_1, ..., init_n, scan_1, ..., scan_m)"
    ""
    "is equivalent to the following pseudo-code:"
    ""
    "    // scan_i.shape[axis_i] denotes the (max) sequence-length of scan_i"
    "    // scan_i.shape[axis_i] is required to be equal to scan_j.shape[axis_j] for all i,j."
    "    sequence_length = scan_1.shape[axis_1];"
    ""
    "    // initialize state-variables"
    "    st_1 = init_1; ... st_n = init_n;"
    "    // initialize scan-output variables: [] denotes an empty tensor"
    "    scan_out_1 = []; ...; scan_out_k = [];"
    "    // identify number of iterations:"
    ""
    "    // execute loop"
    "    for (int t = 0; t < sequence_length; ++t) {"
    "        // generate the scan-input elements: the notation T<axis=k>[t] indicates the sub-tensor"
    "        // of rank one less than T obtained by indexing T at position t along axis k."
    "        si_1 = scan_1<axis=axis_1>[t];"
    "        ... ;"
    "        si_m = scan_m<axis=axis_m>[t];"
    "        // execute loop-body"
    "        st_1, ..., st_n, so_1, ..., so_k = loop-body(st_1, ..., st_n, si_1, ..., si_m)"
    "        // accumulate the scan-output elements"
    "        scan_out_1 = Concat<axis=0>(scan_out_1, so_1); ... ; scan_out_k = Concat<axis=0>(scan_out_k, so_k);"
    "    }"
    ""
    "    return st_1, ..., st_n, scan_out_1, ..., scan_out_k;"
    ""
    "*Sample usage: Encoding RNN using a Scan*"
    ""
    "The following example shows how a simple RNN over an input tensor %X, with weight tensor %Wi,"
    "recurrence weight tensor %Ri, bias tensors %Wbi and %Rbi, and initial hidden-state %H_0 can"
    "be encoded as a ScanLoop. Note that the loop-body is a nested graph, and it directly computes"
    "%Wi, %Ri, %Wbi, and %Rbi (typically constants or initializers in the body graph). If these"
    "values are computed in the outer graph, they need to be passed in as extra state_variables."
    ""
    "    graph rnn-encoding {"
    "      %H_0 = ... "
    "      %X = ..."
    "      %Y_h, %Y = Scan[body = <graph rnn-cell-1>, num_scan_inputs=1](%H_0, %X)"
    "      return %Y, %Y_h"
    "    }"
    ""
    "    graph rnn-cell-1 ("
    "      %H_tminus1[FLOAT, tensor]"
    "      %X_t[FLOAT, tensor]"
    "    ) {"
    "      %Wi = ..."
    "      %Ri = ..."
    "      %Wbi = ..."
    "      %Rbi = ..."
    "      %t1 = X_t * (Wi^T)"
    "      %t2 = H_tminus1*(Ri^T)"
    "      %t3 = Add(%t1, %t2)"
    "      %t4 = Add(%t3, %Wbi)"
    "      %t5 = Add(%t4, %Rbi)"
    "      %Ht = Tanh(%t5)"
    "      %Accumulate = Identity(%Ht)"
    "      return %Ht, %Accumulate"
    "    }"
    ""
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$initial_state_and_scan_inputs);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXScatterOp:ONNX_Op<"Scatter", 
    [NoSideEffect]> {
  let summary = "ONNX Scatter operation";
  let description = [{
    "This operator is deprecated. Please use ScatterElements, which provides the same functionality."
    ""
    "Scatter takes three inputs `data`, `updates`, and `indices` of the same"
    "rank r >= 1 and an optional attribute axis that identifies an axis of `data`"
    "(by default, the outer-most axis, that is axis 0). The output of the operation"
    "is produced by creating a copy of the input `data`, and then updating its value"
    "to values specified by `updates` at specific index positions specified by"
    "`indices`. Its output shape is the same as the shape of `data`."
    ""
    "For each entry in `updates`, the target index in `data` is obtained by combining"
    "the corresponding entry in `indices` with the index of the entry itself: the"
    "index-value for dimension = axis is obtained from the value of the corresponding"
    "entry in `indices` and the index-value for dimension != axis is obtained from the"
    "index of the entry itself."
    ""
    "For instance, in a 2-D tensor case, the update corresponding to the [i][j] entry"
    "is performed as below:"
    "```"
    "  output[indices[i][j]][j] = updates[i][j] if axis = 0, "
    "  output[i][indices[i][j]] = updates[i][j] if axis = 1,"
    "```"
    ""
    "This operator is the inverse of GatherElements. It is similar to Torch's Scatter operation."
    ""
    "Example 1:"
    "```"
    "  data = ["
    "      [0.0, 0.0, 0.0],"
    "      [0.0, 0.0, 0.0],"
    "      [0.0, 0.0, 0.0],"
    "  ]"
    "  indices = ["
    "      [1, 0, 2],"
    "      [0, 2, 1],"
    "  ]"
    "  updates = ["
    "      [1.0, 1.1, 1.2],"
    "      [2.0, 2.1, 2.2],"
    "  ]"
    "  output = ["
    "      [2.0, 1.1, 0.0]"
    "      [1.0, 0.0, 2.2]"
    "      [0.0, 2.1, 1.2]"
    "  ]"
    "```"
    "Example 2:"
    "```"
    "  data = [[1.0, 2.0, 3.0, 4.0, 5.0]]"
    "  indices = [[1, 3]]"
    "  updates = [[1.1, 2.1]]"
    "  axis = 1"
    "  output = [[1.0, 1.1, 3.0, 2.1, 5.0]]"
    "```"
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data, AnyTypeOf<[AnyMemRef, AnyTensor]>:$indices, AnyTypeOf<[AnyMemRef, AnyTensor]>:$updates);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXScatterElementsOp:ONNX_Op<"ScatterElements", 
    [NoSideEffect]> {
  let summary = "ONNX ScatterElements operation";
  let description = [{
    "ScatterElements takes three inputs `data`, `updates`, and `indices` of the same"
    "rank r >= 1 and an optional attribute axis that identifies an axis of `data`"
    "(by default, the outer-most axis, that is axis 0). The output of the operation"
    "is produced by creating a copy of the input `data`, and then updating its value"
    "to values specified by `updates` at specific index positions specified by"
    "`indices`. Its output shape is the same as the shape of `data`."
    ""
    "For each entry in `updates`, the target index in `data` is obtained by combining"
    "the corresponding entry in `indices` with the index of the entry itself: the"
    "index-value for dimension = axis is obtained from the value of the corresponding"
    "entry in `indices` and the index-value for dimension != axis is obtained from the"
    "index of the entry itself."
    ""
    "For instance, in a 2-D tensor case, the update corresponding to the [i][j] entry"
    "is performed as below:"
    "```"
    "  output[indices[i][j]][j] = updates[i][j] if axis = 0, "
    "  output[i][indices[i][j]] = updates[i][j] if axis = 1,"
    "```"
    ""
    "This operator is the inverse of GatherElements. It is similar to Torch's Scatter operation."
    ""
    "Example 1:"
    "```"
    "  data = ["
    "      [0.0, 0.0, 0.0],"
    "      [0.0, 0.0, 0.0],"
    "      [0.0, 0.0, 0.0],"
    "  ]"
    "  indices = ["
    "      [1, 0, 2],"
    "      [0, 2, 1],"
    "  ]"
    "  updates = ["
    "      [1.0, 1.1, 1.2],"
    "      [2.0, 2.1, 2.2],"
    "  ]"
    "  output = ["
    "      [2.0, 1.1, 0.0]"
    "      [1.0, 0.0, 2.2]"
    "      [0.0, 2.1, 1.2]"
    "  ]"
    "```"
    "Example 2:"
    "```"
    "  data = [[1.0, 2.0, 3.0, 4.0, 5.0]]"
    "  indices = [[1, 3]]"
    "  updates = [[1.1, 2.1]]"
    "  axis = 1"
    "  output = [[1.0, 1.1, 3.0, 2.1, 5.0]]"
    "```"
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data, AnyTypeOf<[AnyMemRef, AnyTensor]>:$indices, AnyTypeOf<[AnyMemRef, AnyTensor]>:$updates);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXScatterNDOp:ONNX_Op<"ScatterND", 
    [NoSideEffect]> {
  let summary = "ONNX ScatterND operation";
  let description = [{
    "ScatterND takes three inputs `data` tensor of rank r >= 1, `indices` tensor of rank q >= 1,"
    "and `updates` tensor of rank q + r - indices.shape[-1] - 1. The output of the operation"
    "is produced by creating a copy of the input `data`, and then updating its value to values"
    "specified by `updates` at specific index positions specified by `indices`. Its output shape"
    "is the same as the shape of `data`. Note that `indices` should not have duplicate entries."
    "That is, two or more `updates` for the same index-location is not supported."
    ""
    "`indices` is an integer tensor. Let k denote indices.shape[-1], the last dimension in the shape of `indices`."
    " `indices` is treated as a (q-1)-dimensional tensor of k-tuples, where each k-tuple is a partial-index into `data`."
    "Hence, k can be a value at most the rank of `data`. When k equals rank(data), each update entry specifies an"
    "update to a single element of the tensor. When k is less than rank(data) each update entry specifies an"
    "update to a slice of the tensor."
    ""
    "`updates` is treated as a (q-1)-dimensional tensor of replacement-slice-values. Thus, the"
    "first (q-1) dimensions of updates.shape must match the first (q-1) dimensions of indices.shape."
    "The remaining dimensions of `updates` correspond to the dimensions of the"
    "replacement-slice-values. Each replacement-slice-value is a (r-k) dimensional tensor,"
    "corresponding to the trailing (r-k) dimensions of `data`.  Thus, the shape of `updates`"
    "must equal indices.shape[0:q-1] ++ data.shape[k:r-1], where ++ denotes the concatenation"
    "of shapes."
    ""
    "The `output` is calculated via the following equation:"
    ""
    "    output = np.copy(data)"
    "    update_indices = indices.shape[:-1]"
    "    for idx in np.ndindex(update_indices):"
    "        output[indices[idx]] = updates[idx]"
    ""
    "The order of iteration in the above loop is not specified."
    "In particular, indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2]."
    "This ensures that the output value does not depend on the iteration order."
    ""
    "This operator is the inverse of GatherND."
    ""
    "Example 1:"
    "```"
    "  data    = [1, 2, 3, 4, 5, 6, 7, 8]"
    "  indices = [[4], [3], [1], [7]]"
    "  updates = [9, 10, 11, 12]"
    "  output  = [1, 11, 3, 10, 9, 6, 7, 12]"
    "```"
    ""
    "Example 2:"
    "```"
    "  data    = [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],"
    "             [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],"
    "             [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],"
    "             [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]"
    "  indices = [[0], [2]]"
    "  updates = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],"
    "             [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]]"
    "  output  = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],"
    "             [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],"
    "             [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],"
    "             [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]"
    "```"
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data, AnyTypeOf<[AnyMemRef, AnyTensor]>:$indices, AnyTypeOf<[AnyMemRef, AnyTensor]>:$updates);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXSeluOp:ONNX_Op<"Selu", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let summary = "ONNX Selu operation";
  let description = [{
    "Selu takes one input data (Tensor<T>) and produces one output data"
    "(Tensor<T>) where the scaled exponential linear unit function,"
    "`y = gamma * (alpha * e^x - alpha) for x <= 0`, `y = gamma * x for x > 0`,"
    "is applied to the tensor elementwise."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXSequenceAtOp:ONNX_Op<"SequenceAt", 
    [NoSideEffect]> {
  let summary = "ONNX SequenceAt operation";
  let description = [{
    "Outputs a tensor copy from the tensor at 'position' in 'input_sequence'."
    "Accepted range for 'position' is in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'."
    "Negative value means counting positions from the back."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input_sequence, AnyTypeOf<[AnyMemRef, AnyTensor]>:$position);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXSequenceConstructOp:ONNX_Op<"SequenceConstruct", 
    [NoSideEffect]> {
  let summary = "ONNX SequenceConstruct operation";
  let description = [{
    "Construct a tensor sequence containing 'inputs' tensors."
    "All tensors in 'inputs' must have the same data type."
  }];
  let arguments = (ins Variadic<AnyTypeOf<[AnyMemRef, AnyTensor]>>:$inputs);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXSequenceEmptyOp:ONNX_Op<"SequenceEmpty", 
    [NoSideEffect]> {
  let summary = "ONNX SequenceEmpty operation";
  let description = [{
    "Construct an empty tensor sequence, with given data type."
  }];
  let arguments = (ins );
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXSequenceEraseOp:ONNX_Op<"SequenceErase", 
    [NoSideEffect]> {
  let summary = "ONNX SequenceErase operation";
  let description = [{
    "Outputs a tensor sequence that removes the tensor at 'position' from 'input_sequence'."
    "Accepted range for 'position' is in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'."
    "Negative value means counting positions from the back."
    "'position' is optional, by default it erases the last tensor from 'input_sequence'."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input_sequence, AnyTypeOf<[AnyMemRef, AnyTensor]>:$position);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXSequenceInsertOp:ONNX_Op<"SequenceInsert", 
    [NoSideEffect]> {
  let summary = "ONNX SequenceInsert operation";
  let description = [{
    "Outputs a tensor sequence that inserts 'tensor' into 'input_sequence' at 'position'."
    "'tensor' must have the same data type as 'input_sequence'."
    "Accepted range for 'position' is in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'."
    "Negative value means counting positions from the back."
    "'position' is optional, by default it inserts 'tensor' to the back of 'input_sequence'."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input_sequence, AnyTypeOf<[AnyMemRef, AnyTensor]>:$tensor, AnyTypeOf<[AnyMemRef, AnyTensor]>:$position);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXSequenceLengthOp:ONNX_Op<"SequenceLength", 
    [NoSideEffect]> {
  let summary = "ONNX SequenceLength operation";
  let description = [{
    "Produces a scalar(tensor of empty shape) containing the number of tensors in 'input_sequence'."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input_sequence);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXShapeOp:ONNX_Op<"Shape", 
    [NoSideEffect]> {
  let summary = "ONNX Shape operation";
  let description = [{
    "Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXShrinkOp:ONNX_Op<"Shrink", 
    [NoSideEffect]> {
  let summary = "ONNX Shrink operation";
  let description = [{
    "Shrink takes one input data (Tensor<numeric>) and produces one Tensor output,"
    "having same datatype and shape with input. It has two attributes, lambd and"
    "bias. The formula of this operator is: If x < -lambd, y = x + bias;"
    "If x > lambd, y = x - bias; Otherwise, y = 0."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXSigmoidOp:ONNX_Op<"Sigmoid", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let summary = "ONNX Sigmoid operation";
  let description = [{
    "Sigmoid takes one input data (Tensor<T>) and produces one output data"
    "(Tensor<T>) where the sigmoid function, y = 1 / (1 + exp(-x)), is applied to the"
    "tensor elementwise."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXSignOp:ONNX_Op<"Sign", 
    [NoSideEffect]> {
  let summary = "ONNX Sign operation";
  let description = [{
    "Calculate the sign of the given input tensor element-wise."
    "If input > 0, output 1. if input < 0, output -1. if input == 0, output 0."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXSinOp:ONNX_Op<"Sin", 
    [NoSideEffect]> {
  let summary = "ONNX Sin operation";
  let description = [{
    "Calculates the sine of the given input tensor, element-wise."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXSinhOp:ONNX_Op<"Sinh", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let summary = "ONNX Sinh operation";
  let description = [{
    "Calculates the hyperbolic sine of the given input tensor element-wise."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXSizeOp:ONNX_Op<"Size", 
    [NoSideEffect]> {
  let summary = "ONNX Size operation";
  let description = [{
    "Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input tensor."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXSliceOp:ONNX_Op<"Slice", 
    [NoSideEffect]> {
  let summary = "ONNX Slice operation";
  let description = [{
    "Produces a slice of the input tensor along multiple axes. Similar to numpy:"
    "https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html"
    "Slices uses `starts`, `ends`, `axes` and `steps` inputs to specify the start and end"
    "dimension and step for each axis in the list of axes, it uses this information to"
    "slice the input `data` tensor. If a negative value is passed for any of the"
    "start or end indices, it represents number of elements before the end of that"
    "dimension. If the value passed to start or end is larger than the `n` (the"
    "number of elements in this dimension), it represents `n`. For slicing to the"
    "end of a dimension with unknown size, it is recommended to pass in `INT_MAX` "
    "when sclicing forward and 'INT_MIN' when slicing backward."
    "If a negative value is passed for step, it represents slicing backward. "
    "However step value cannot be 0."
    "If `axes` are omitted, they are set to `[0, ..., ndim-1]`."
    "If `steps` are omitted, they are set to `[1, ..., 1]` of length `len(starts)`"
    "Example 1:"
    "  data = ["
    "      [1, 2, 3, 4],"
    "      [5, 6, 7, 8],"
    "  ]"
    "  axes = [0, 1]"
    "  starts = [1, 0]"
    "  ends = [2, 3]"
    "  steps = [1, 2]"
    "  result = ["
    "      [5, 7],"
    "  ]"
    "Example 2:"
    "  data = ["
    "      [1, 2, 3, 4],"
    "      [5, 6, 7, 8],"
    "  ]"
    "  starts = [0, 1]"
    "  ends = [-1, 1000]"
    "  result = ["
    "      [2, 3, 4],"
    "  ]"
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data, AnyTypeOf<[AnyMemRef, AnyTensor]>:$starts, AnyTypeOf<[AnyMemRef, AnyTensor]>:$ends, AnyTypeOf<[AnyMemRef, AnyTensor]>:$axes, AnyTypeOf<[AnyMemRef, AnyTensor]>:$steps);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXSoftmaxOp:ONNX_Op<"Softmax", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let summary = "ONNX Softmax operation";
  let description = [{
    "The operator computes the softmax (normalized exponential) values for each layer in the batch"
    " of the given input."
    ""
    "The input does not need to explicitly be a 2D vector; rather, it will be"
    "coerced into one. For an arbitrary n-dimensional tensor"
    "input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1\}\] and k is"
    "the axis provided, then input will be coerced into a 2-dimensional tensor with"
    "dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1\}\]. For the default"
    "case where axis=1, this means the input tensor will be coerced into a 2D tensor"
    "of dimensions [a_0, a_1 * ... * a_{n-1\}\], where a_0 is often the batch size."
    "In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D."
    "Each of these dimensions must be matched correctly, or else the operator"
    "will throw errors. The output tensor has the same shape"
    "and contains the softmax values of the corresponding input."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXSoftplusOp:ONNX_Op<"Softplus", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let summary = "ONNX Softplus operation";
  let description = [{
    "Softplus takes one input data (Tensor<T>) and produces one output data"
    "(Tensor<T>) where the softplus function, y = ln(exp(x) + 1), is applied to"
    "the tensor elementwise."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXSoftsignOp:ONNX_Op<"Softsign", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let summary = "ONNX Softsign operation";
  let description = [{
    "Calculates the softsign (x/(1+|x|)) of the given input tensor element-wise."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXSpaceToDepthOp:ONNX_Op<"SpaceToDepth", 
    [NoSideEffect]> {
  let summary = "ONNX SpaceToDepth operation";
  let description = [{
    "SpaceToDepth rearranges blocks of spatial data into depth. More specifically,"
    "this op outputs a copy of the input tensor where values from the height and width dimensions"
    "are moved to the depth dimension."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXSplitOp:ONNX_Op<"Split", 
    [NoSideEffect]> {
  let summary = "ONNX Split operation";
  let description = [{
    "Split a tensor into a list of tensors, along the specified"
    "'axis'. Lengths of the parts can be specified using argument 'split'."
    "Otherwise, the tensor is split to equal sized parts."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXSplitToSequenceOp:ONNX_Op<"SplitToSequence", 
    [NoSideEffect]> {
  let summary = "ONNX SplitToSequence operation";
  let description = [{
    "Split a tensor into a sequence of tensors, along the specified"
    "'axis'. Lengths of the parts can be specified using argument 'split'."
    "'split' must contain only positive numbers."
    "'split' is either a scalar (tensor of empty shape), or a 1-D tensor."
    "If 'split' is a scalar, then 'input' will be split into equally sized chunks(if possible)."
    "Last chunk will be smaller if the 'input' size along the given axis 'axis' is not divisible"
    "by 'split'."
    "Otherwise, the tensor is split into 'size(split)' chunks, with lengths of the parts on 'axis'"
    "specified in 'split'. In this scenario, the sum of entries in 'split' must be equal to the"
    "dimension size of input tensor on 'axis'."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input, AnyTypeOf<[AnyMemRef, AnyTensor]>:$split);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXSqrtOp:ONNX_Op<"Sqrt", 
    [NoSideEffect]> {
  let summary = "ONNX Sqrt operation";
  let description = [{
    "Square root takes one input data (Tensor<T>) and produces one output data"
    "(Tensor<T>) where the square root is, y = x^0.5, is applied to"
    "the tensor elementwise. If x is negative, then it will return NaN."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXSqueezeOp:ONNX_Op<"Squeeze", 
    [NoSideEffect]> {
  let summary = "ONNX Squeeze operation";
  let description = [{
    "Remove single-dimensional entries from the shape of a tensor."
    "Takes a  parameter `axes` with a list of axes to squeeze."
    "If `axes` is not provided, all the single dimensions will be removed from"
    "the shape. If an axis is selected with shape entry not equal to one, an error is raised."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXStringNormalizerOp:ONNX_Op<"StringNormalizer", 
    [NoSideEffect]> {
  let summary = "ONNX StringNormalizer operation";
  let description = [{
    "StringNormalization performs string operations for basic cleaning."
    "This operator has only one input (denoted by X) and only one output"
    "(denoted by Y). This operator first examines the elements in the X,"
    "and removes elements specified in "stopwords" attribute."
    "After removing stop words, the intermediate result can be further lowercased,"
    "uppercased, or just returned depending the "case_change_action" attribute."
    "This operator only accepts [C]- and [1, C]-tensor."
    "If all elements in X are dropped, the output will be the empty value of string tensor with shape [1]"
    "if input shape is [C] and shape [1, 1] if input shape is [1, C]."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXSubOp:ONNX_Op<"Sub", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let summary = "ONNX Sub operation";
  let description = [{
    "Performs element-wise binary subtraction (with Numpy-style broadcasting support)."
    ""
    "This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md)."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$A, AnyTypeOf<[AnyMemRef, AnyTensor]>:$B);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXSumOp:ONNX_Op<"Sum", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let summary = "ONNX Sum operation";
  let description = [{
    "Element-wise sum of each of the input tensors (with Numpy-style broadcasting support)."
    "All inputs and outputs must have the same data type."
    "This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md)."
  }];
  let arguments = (ins Variadic<AnyTypeOf<[AnyMemRef, AnyTensor]>>:$data_0);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXTanOp:ONNX_Op<"Tan", 
    [NoSideEffect]> {
  let summary = "ONNX Tan operation";
  let description = [{
    "Calculates the tangent of the given input tensor, element-wise."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXTanhOp:ONNX_Op<"Tanh", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let summary = "ONNX Tanh operation";
  let description = [{
    "Calculates the hyperbolic tangent of the given input tensor element-wise."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXTfIdfVectorizerOp:ONNX_Op<"TfIdfVectorizer", 
    [NoSideEffect]> {
  let summary = "ONNX TfIdfVectorizer operation";
  let description = [{
    "This transform extracts n-grams from the input sequence and save them as a vector. Input can"
    "be either a 1-D or 2-D tensor. For 1-D input, output is the n-gram representation of that input."
    "For 2-D input, the output is also a  2-D tensor whose i-th row is the n-gram representation of the i-th input row."
    "More specifically, if input shape is [C], the corresponding output shape would be [max(ngram_indexes) + 1]."
    "If input shape is [N, C], this operator produces a [N, max(ngram_indexes) + 1]-tensor."
    ""
    "In contrast to standard n-gram extraction, here, the indexes of extracting an n-gram from the original"
    "sequence are not necessarily consecutive numbers. The discontinuity between indexes are controlled by the number of skips."
    "If the number of skips is 2, we should skip two tokens when scanning through the original sequence."
    "Let's consider an example. Assume that input sequence is [94, 17, 36, 12, 28] and the number of skips is 2."
    "The associated 2-grams are [94, 12] and [17, 28] respectively indexed by [0, 3] and [1, 4]."
    "If the number of skips becomes 0, the 2-grams generated are [94, 17], [17, 36], [36, 12], [12, 28]"
    "indexed by [0, 1], [1, 2], [2, 3], [3, 4], respectively."
    ""
    "The output vector (denoted by Y) stores the count of each n-gram;"
    "Y[ngram_indexes[i]] indicates the times that the i-th n-gram is found. The attribute ngram_indexes is used to determine the mapping"
    "between index i and the corresponding n-gram's output coordinate. If pool_int64s is [94, 17, 17, 36], ngram_indexes is [1, 0],"
    "ngram_counts=[0, 0], then the Y[0] (first element in Y) and Y[1] (second element in Y) are the counts of [17, 36] and [94, 17],"
    "respectively. An n-gram which cannot be found in pool_strings/pool_int64s should be ignored and has no effect on the output."
    "Note that we may consider all skips up to S when generating the n-grams."
    ""
    "The examples used above are true if mode is "TF". If mode is "IDF", all the counts larger than 1 would be truncated to 1 and"
    "the i-th element in weights would be used to scale (by multiplication) the count of the i-th n-gram in pool. If mode is "TFIDF","
    "this operator first computes the counts of all n-grams and then scale them by the associated values in the weights attribute."
    ""
    "Only one of pool_strings and pool_int64s can be set. If pool_int64s is set, the input should be an integer tensor."
    "If pool_strings is set, the input must be a string tensor."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXThresholdedReluOp:ONNX_Op<"ThresholdedRelu", 
    [NoSideEffect]> {
  let summary = "ONNX ThresholdedRelu operation";
  let description = [{
    "ThresholdedRelu takes one input data (Tensor<T>) and produces one output data"
    "(Tensor<T>) where the rectified linear function, y = x for x > alpha, y = 0 otherwise,"
    "is applied to the tensor elementwise."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXTileOp:ONNX_Op<"Tile", 
    [NoSideEffect]> {
  let summary = "ONNX Tile operation";
  let description = [{
    "Constructs a tensor by tiling a given tensor."
    "This is the same as function `tile` in Numpy, but no broadcast."
    "For example A = [[1, 2], [3, 4]], B = [1, 2], tile(A, B) = [[1, 2, 1, 2], [3, 4, 3, 4]]"
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input, AnyTypeOf<[AnyMemRef, AnyTensor]>:$repeats);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXTopKOp:ONNX_Op<"TopK", 
    [NoSideEffect]> {
  let summary = "ONNX TopK operation";
  let description = [{
    "Retrieve the top-K largest or smallest elements along a specified axis. Given an input tensor of"
    "shape [a_1, a_2, ..., a_n, r] and integer argument k, return two outputs:"
    "  -Value tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n]"
    "    which contains the values of the top k elements along the specified axis"
    "  -Index tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] which"
    "   contains the indices of the top k elements (original indices from the input"
    "   tensor)."
    ""
    "If "largest" is 1 (the default value) then the k largest elements are returned."
    "If "sorted" is 1 (the default value) then the resulting k elements will be sorted."
    "If "sorted" is 0, order of returned 'Values' and 'Indices' are undefined."
    ""
    "Given two equivalent values, this operator uses the indices along the axis as"
    " a tiebreaker. That is, the element with the lower index will appear first."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X, AnyTypeOf<[AnyMemRef, AnyTensor]>:$K);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>, AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXTransposeOp:ONNX_Op<"Transpose", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let summary = "ONNX Transpose operation";
  let description = [{
    "Transpose the input tensor similar to numpy.transpose. For example, when"
    "perm=(1, 0, 2), given an input tensor of shape (1, 2, 3), the output shape"
    "will be (2, 1, 3)."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);

  let extraClassDeclaration = [{
    static StringRef getPermAttrName() { return "perm"; }
  }];

  let verifier = [{ return ::verify(*this); }];
}

def ONNXUniqueOp:ONNX_Op<"Unique", 
    [NoSideEffect]> {
  let summary = "ONNX Unique operation";
  let description = [{
    "Find the unique elements of a tensor. When an optional attribute 'axis' is provided, unique subtensors sliced along the 'axis' are returned. "
    "Otherwise the input tensor is flattened and unique values of the flattened tensor are returned. "
    ""
    "This operator returns the unique values or sliced unique subtensors of the input tensor and three optional outputs. "
    "The first output tensor 'Y' contains all unique values or subtensors of the input. "
    "The second optional output tensor 'indices' contains indices of 'Y' elements' first occurance in 'X'.. "
    "The third optional output tensor 'inverse_indices' contains, for elements of 'X', its corresponding indices in 'Y'. ". "
    "The fourth optional output tensor 'counts' contains the count of each element of 'Y' in the input. "
    ""
    "Outputs are either sorted in ascending order or optionally in the order of the first occurrence of the values in the input. "
    ""
    "https://docs.scipy.org/doc/numpy/reference/generated/numpy.unique.html"
    ""
    "Example 1:"
    "  input_X = [2, 1, 1, 3, 4, 3]"
    "  attribute_sorted = 0"
    "  attribute_axis = None"
    "  output_Y = [2, 1, 3, 4]"
    "  output_indices = [0, 1, 3, 4]"
    "  output_inverse_indices = [0, 1, 1, 2, 3, 2]"
    "  output_counts = [1, 2, 2, 1]"
    ""
    "Example 2:"
    "  input_X = [[1, 3], [2, 3]]"
    "  attribute_sorted = 1"
    "  attribute_axis = None"
    "  output_Y = [1, 2, 3]"
    "  output_indices = [0, 2, 1]"
    "  output_inverse_indices = [0, 2, 1, 2]"
    "  output_counts = [1, 1, 2]"
    ""
    "Example 3:"
    "  input_X = [[1, 0, 0], [1, 0, 0], [2, 3, 4]]"
    "  attribute_sorted = 1"
    "  attribute_axis = 0"
    "  output_Y = [[1, 0, 0], [2, 3, 4]]"
    "  output_indices = [0, 2]"
    "  output_inverse_indices = [0, 0, 1]"
    "  output_counts = [2, 1]"
    ""
    "Example 4:"
    "  input_x = [[[1., 1.], [0., 1.], [2., 1.], [0., 1.]], "
    "             [[1., 1.], [0., 1.], [2., 1.], [0., 1.]]]"
    "  attribute_sorted = 1"
    "  attribute_axis = 1"
    ""
    "  intermediate data are presented below for better understanding: "
    "  "
    "  there are 4 subtensors sliced along axis 1 of input_x (shape = (2, 4, 2)):"
    "  A: [[1, 1], [1, 1]], "
    "     [[0, 1], [0, 1]], "
    "     [[2, 1], [2, 1]], "
    "     [[0, 1], [0, 1]]."
    "  "
    "  there are 3 unique subtensors: "
    "  [[1, 1], [1, 1]], "
    "  [[0, 1], [0, 1]], "
    "  [[2, 1], [2, 1]]."
    "  "
    "  sorted unique subtensors:"
    "  B: [[0, 1], [0, 1]], "
    "     [[1, 1], [1, 1]], "
    "     [[2, 1], [2, 1]]."
    "  "
    "  output_Y is constructed from B:"
    "  [[[0. 1.], [1. 1.], [2. 1.]], "
    "   [[0. 1.], [1. 1.], [2. 1.]]]"
    ""
    "  output_indices is to map from B to A:"
    "  [1, 0, 2]"
    "  "
    "  output_inverse_indices is to map from A to B:"
    "  [1, 0, 2, 0]"
    ""
    "  output_counts = [2 1 1]"
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>, AnyTypeOf<[AnyMemRef, AnyTensor]>, AnyTypeOf<[AnyMemRef, AnyTensor]>, AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXUnsqueezeOp:ONNX_Op<"Unsqueeze", 
    [NoSideEffect]> {
  let summary = "ONNX Unsqueeze operation";
  let description = [{
    "Insert single-dimensional entries to the shape of an input tensor (`data`)."
    "Takes one required argument `axes` - which contains a list of dimension indices and this operator will insert a dimension of value `1` into the corresponding index of the output tensor (`expanded`)."
    ""
    "For example:"
    "  Given an input tensor (`data`) of shape [3, 4, 5], then"
    "  Unsqueeze(data, axes=[0, 4]) outputs a tensor (`expanded`) containing same data as `data` but with shape [1, 3, 4, 5, 1]."
    ""
    "The attribute `axes` should not contain any duplicate entries. It is an error if it contains duplicates."
    "The rank of the output tensor (`output_rank`) is the rank of the input tensor (`data`) plus the number of values in `axes`."
    "Each value in `axes` should be within the (inclusive) range [-output_rank , output_rank - 1]. "
    "The order of values in `axes` does not matter and can come in any order. "
    ""
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXUpsampleOp:ONNX_Op<"Upsample", 
    [NoSideEffect]> {
  let summary = "ONNX Upsample operation";
  let description = [{
    "Upsample the input tensor."
    "Each dimension value of the output tensor is:"
    "  output_dimension = floor(input_dimension * scale)."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X, AnyTypeOf<[AnyMemRef, AnyTensor]>:$scales);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXWhereOp:ONNX_Op<"Where", 
    [NoSideEffect]> {
  let summary = "ONNX Where operation";
  let description = [{
    "Return elements, either from X or Y, depending on condition"
    "    (with Numpy-style broadcasting support)."
    "    Where behaves like numpy.where with three parameters:"
    "    https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html"
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$condition, AnyTypeOf<[AnyMemRef, AnyTensor]>:$X, AnyTypeOf<[AnyMemRef, AnyTensor]>:$Y);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}

def ONNXXorOp:ONNX_Op<"Xor", 
    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
  let summary = "ONNX Xor operation";
  let description = [{
    "Returns the tensor resulted from performing the `xor` logical operation"
    "elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support)."
    ""
    "This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md)."
  }];
  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$A, AnyTypeOf<[AnyMemRef, AnyTensor]>:$B);
  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
}