parameters: (x, y, name=None)
return_statement: return _elementwise_op(LayerHelper(op_type, **locals()))
docstring: r"""
      Mod two tensors element-wise. The equation is:
  
      .. math::
  
          out = x \% y
  
      **Note**:
      ``paddle.remainder`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
  
      Args:
          x (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
          y (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
          name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
  
      Returns:
          N-D Tensor. A location into which the result is stored. If x, y have different shapes and are "broadcastable", the resulting tensor shape is the shape of x and y after broadcasting. If x, y have the same shape,  its shape is the same as x and y.
  
      Examples:
  
          ..  code-block:: python
  
              import paddle
  
              x = paddle.to_tensor([2, 3, 8, 7])
              y = paddle.to_tensor([1, 5, 3, 3])
              z = paddle.remainder(x, y)
              print(z)  # [0, 3, 2, 1]
docstring_summary: r"""
      Mod two tensors element-wise. The equation is:
docstring_tokens: r Mod two tensors element - wise . The equation is :
function: def remainder(x, y, name=None):
      r"""
      Mod two tensors element-wise. The equation is:
  
      .. math::
  
          out = x \% y
  
      **Note**:
      ``paddle.remainder`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
  
      Args:
          x (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
          y (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
          name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
  
      Returns:
          N-D Tensor. A location into which the result is stored. If x, y have different shapes and are "broadcastable", the resulting tensor shape is the shape of x and y after broadcasting. If x, y have the same shape,  its shape is the same as x and y.
  
      Examples:
  
          ..  code-block:: python
  
              import paddle
  
              x = paddle.to_tensor([2, 3, 8, 7])
              y = paddle.to_tensor([1, 5, 3, 3])
              z = paddle.remainder(x, y)
              print(z)  # [0, 3, 2, 1]
  
      """
      op_type = 'elementwise_mod'
      axis = -1
      if in_dygraph_mode():
          return _elementwise_op_in_dygraph(
              x, y, axis=axis, op_name=op_type)
  
      return _elementwise_op(LayerHelper(op_type, **locals()))
function_tokens: def remainder ( x , y , name = None ) : op_type = 'elementwise_mod' axis = - 1 if in_dygraph_mode ( ) : return _elementwise_op_in_dygraph ( x , y , axis = axis , op_name = op_type ) return _elementwise_op ( LayerHelper ( op_type , * * locals ( ) ) )