parameters: (x, y, name=None)
return_statement: return _elementwise_op(LayerHelper(op_type, **locals()))
docstring: multiply two tensors element-wise. The equation is:
  
      .. math::
          out = x * y
  
      **Note**:
      ``paddle.multiply`` supports broadcasting. If you would like to know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
  
      Args:
          x (Tensor): the input tensor, its data type should be one of float32, float64, int32, int64, bool.
          y (Tensor): the input tensor, its data type should be one of float32, float64, int32, int64, bool.
          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([[1, 2], [3, 4]])
              y = paddle.to_tensor([[5, 6], [7, 8]])
              res = paddle.multiply(x, y)
              print(res) # [[5, 12], [21, 32]]
  
              x = paddle.to_tensor([[[1, 2, 3], [1, 2, 3]]])
              y = paddle.to_tensor([2])
              res = paddle.multiply(x, y)
              print(res) # [[[2, 4, 6], [2, 4, 6]]]
docstring_summary: multiply two tensors element-wise. The equation is:
docstring_tokens: multiply two tensors element - wise . The equation is :
function: def multiply(x, y, name=None):
      """
      multiply two tensors element-wise. The equation is:
  
      .. math::
          out = x * y
  
      **Note**:
      ``paddle.multiply`` supports broadcasting. If you would like to know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
  
      Args:
          x (Tensor): the input tensor, its data type should be one of float32, float64, int32, int64, bool.
          y (Tensor): the input tensor, its data type should be one of float32, float64, int32, int64, bool.
          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([[1, 2], [3, 4]])
              y = paddle.to_tensor([[5, 6], [7, 8]])
              res = paddle.multiply(x, y)
              print(res) # [[5, 12], [21, 32]]
  
              x = paddle.to_tensor([[[1, 2, 3], [1, 2, 3]]])
              y = paddle.to_tensor([2])
              res = paddle.multiply(x, y)
              print(res) # [[[2, 4, 6], [2, 4, 6]]]
  
      """
      op_type = 'elementwise_mul'
      act = None
      axis = -1
  
      if in_dygraph_mode():
          return _elementwise_op_in_dygraph(
              x, y, axis=axis, act=act, op_name=op_type)
  
      if x.dtype != y.dtype:
          raise TypeError(
              'Input tensors must be same type, but received type of x: %s, type of y: %s '
              % (x.dtype, y.dtype))
  
      return _elementwise_op(LayerHelper(op_type, **locals()))
function_tokens: def multiply ( x , y , name = None ) : op_type = 'elementwise_mul' act = None axis = - 1 if in_dygraph_mode ( ) : return _elementwise_op_in_dygraph ( x , y , axis = axis , act = act , op_name = op_type ) if x . dtype != y . dtype : raise TypeError ( 'Input tensors must be same type, but received type of x: %s, type of y: %s ' % ( x . dtype , y . dtype ) ) return _elementwise_op ( LayerHelper ( op_type , * * locals ( ) ) )