text: /**
   * Copyright 2019-2020 Huawei Technologies Co., Ltd
   *
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   */
  
  #ifndef GE_OP_MATH_OPS_H_
  #define GE_OP_MATH_OPS_H_
  
  #include "graph/operator_reg.h"
  #include "graph/operator.h"
  
  namespace ge {
  
  REG_OP(Igamma)
      .INPUT(a, TensorType({DT_FLOAT, DT_DOUBLE}))
      .INPUT(x, TensorType({DT_FLOAT, DT_DOUBLE}))
      .OUTPUT(z, TensorType({DT_FLOAT, DT_DOUBLE}))
      .OP_END_FACTORY_REG(Igamma)
  
  REG_OP(Igammac)
      .INPUT(a, TensorType({DT_FLOAT, DT_DOUBLE}))
      .INPUT(x, TensorType({DT_FLOAT, DT_DOUBLE}))
      .OUTPUT(z, TensorType({DT_FLOAT, DT_DOUBLE}))
      .OP_END_FACTORY_REG(Igammac)
  
  REG_OP(CompareAndBitpack)
      .INPUT(x, TensorType({ DT_FLOAT, DT_FLOAT16, DT_DOUBLE, DT_INT8, \
          DT_INT16, DT_INT32, DT_INT64, DT_BOOL }))
      .INPUT(threshold, TensorType({ DT_FLOAT, DT_FLOAT16, DT_DOUBLE, \
          DT_INT8, DT_INT16, DT_INT32, DT_INT64, DT_BOOL }))
      .OUTPUT(y, TensorType(DT_UINT8))
      .OP_END_FACTORY_REG(CompareAndBitpack)
  
  REG_OP(Bincount)
      .INPUT(array, TensorType(DT_INT32))
      .INPUT(size, TensorType(DT_INT32))
      .INPUT(weights, TensorType({ DT_FLOAT, DT_INT32, DT_INT64, DT_DOUBLE }))
      .OUTPUT(bins, TensorType({ DT_FLOAT, DT_INT32, DT_INT64, DT_DOUBLE }))
      .OP_END_FACTORY_REG(Bincount)
  
  REG_OP(Betainc)
      .INPUT(a, TensorType({DT_DOUBLE, DT_FLOAT}))
      .INPUT(b, TensorType({DT_DOUBLE, DT_FLOAT}))
      .INPUT(x, TensorType({DT_DOUBLE, DT_FLOAT}))
      .OUTPUT(z, TensorType({DT_DOUBLE, DT_FLOAT}))
      .OP_END_FACTORY_REG(Betainc)
  
  REG_OP(Zeta)
      .INPUT(x, TensorType({DT_DOUBLE, DT_FLOAT}))
      .INPUT(q, TensorType({DT_DOUBLE, DT_FLOAT}))
      .OUTPUT(z, TensorType({DT_DOUBLE, DT_FLOAT}))
      .OP_END_FACTORY_REG(Zeta)
  
  REG_OP(Bucketize)
      .INPUT(x, TensorType({DT_INT32, DT_INT64, DT_DOUBLE, DT_FLOAT}))
      .OUTPUT(y, TensorType({DT_INT32}))
      .REQUIRED_ATTR(boundaries, ListFloat)
      .OP_END_FACTORY_REG(Bucketize)
  
  REG_OP(SparseSegmentSum)
      .INPUT(x, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_UINT16,
          DT_INT32, DT_INT64, DT_DOUBLE, DT_FLOAT, DT_FLOAT16}))
      .INPUT(indices, TensorType({DT_INT32}))
      .INPUT(segment_ids, TensorType({DT_INT32}))
      .OUTPUT(y, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_UINT16,
          DT_INT32, DT_INT64, DT_DOUBLE, DT_FLOAT, DT_FLOAT16}))
      .OP_END_FACTORY_REG(SparseSegmentSum)
  
  REG_OP(SparseSegmentMean)
      .INPUT(x, TensorType({DT_FLOAT, DT_DOUBLE}))
      .INPUT(indices, TensorType({DT_INT32}))
      .INPUT(segment_ids, TensorType({DT_INT32}))
      .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE}))
      .OP_END_FACTORY_REG(SparseSegmentMean)
  
  REG_OP(SparseSegmentMeanGrad)
      .INPUT(x, TensorType({DT_FLOAT, DT_DOUBLE}))
      .INPUT(indices, TensorType({DT_INT32}))
      .INPUT(segment_ids, TensorType({DT_INT32}))
      .INPUT(output_dim0, TensorType({DT_INT32}))
      .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE}))
      .OP_END_FACTORY_REG(SparseSegmentMeanGrad)
  
  REG_OP(IgammaGradA)
      .INPUT(a, TensorType({DT_FLOAT, DT_DOUBLE}))
      .INPUT(x, TensorType({DT_FLOAT, DT_DOUBLE}))
      .OUTPUT(z, TensorType({DT_FLOAT, DT_DOUBLE}))
      .OP_END_FACTORY_REG(IgammaGradA)
  
  REG_OP(InitData)
      .ATTR(channel_name, String, "")
      .OP_END_FACTORY_REG(InitData)
  
  REG_OP(GetNext)
      .DYNAMIC_OUTPUT(y, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, DT_INT32, DT_INT64, DT_UINT32, DT_UINT64,
                                          DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_BOOL}))
      .ATTR(output_types, ListInt, {})
      .ATTR(output_shapes, ListListInt, {})
      .ATTR(output_num, Int, 1)
      .ATTR(channel_name, String, "")
      .OP_END_FACTORY_REG(GetNext)
  /**
  *@brief: Computes the Gauss error function of `x` element-wise.
  
  *@par Inputs:\n
  *x: A Tensor of type float16 or float32.
  
  *@par Outputs:
  *y: A Tensor. Has the same type as "x".
  */
  REG_OP(Erf)
      .INPUT(x, TensorType::FloatingDataType())
      .OUTPUT(y, TensorType::FloatingDataType())
      .OP_END_FACTORY_REG(Erf)
  
  /**
  *@brief: Computes the Gauss complementary error function of "x" element-wise.
  
  *@par Inputs:\n
  *x: A Tensor of type float16 or float32.
  
  *@par Outputs:
  *y: A Tensor. Has the same type as "x".
  */
  REG_OP(Erfc)
      .INPUT(x, TensorType::FloatingDataType())
      .OUTPUT(y, TensorType::FloatingDataType())
      .OP_END_FACTORY_REG(Erfc)
  
  /**
  *@brief This operation returns a rank 1 histogram counting the number of entries in `values` \n
  *  that fell into every bin.The bins are equal width and determined by the arguments \n
  *  'value_range' and 'nbins'. \n
  
  *@par Inputs:
  *Three inputs, including: \n
  *@li x: A Tensor of type float32,float16,int32.
  *@li range: A Tensor of type float32,float16,int32.
  *@li nbins: A Tensor of type int32.
  
  *@par Attributes:
  * dtype: An optional attribute. Defaults to "int32".
  
  *@par Outputs:
  *y: A Tensor. A Tensor of type int32.
  */
  REG_OP(HistogramFixedWidth)
      .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))
      .INPUT(range, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))
      .INPUT(nbins, TensorType({DT_INT32}))
      .OUTPUT(y, TensorType({DT_INT32}))
      .ATTR(dtype, String, "int32")
      .OP_END_FACTORY_REG(HistogramFixedWidth)
  
  /**
  *@brief This operation returns a rank 1 histogram counting the number of entries in `values` \n
  *  that fell into every bin.The bins are equal width and determined by the arguments \n
  *  'value_range' and 'nbins'. \n
  
  *@par Inputs:
  *Two inputs, including: \n
  *@li x: A Tensor of type float32,float16,int32.
  *@li range: A Tensor of type float32,float16,int32.
  
  *@par Attributes:
  *@li dtype: An optional attribute. Defaults to "int32".
  *@li nbins: A required attribute,the type is int32.
  
  *@par Outputs:
  *y: A Tensor. A Tensor of type int32.
  */
  REG_OP(HistogramFixedWidthD)
      .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))
      .INPUT(range, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))
      .OUTPUT(y, TensorType({DT_INT32}))
      .REQUIRED_ATTR(nbins, Int)
      .ATTR(dtype, String, "int32")
      .OP_END_FACTORY_REG(HistogramFixedWidthD)
  }  // namespace ge
  
  #endif  // GE_OP_MATH_OPS_H_
  