parameters: (x, y, name=None)
return_statement: return out
docstring: Outer product of two Tensors.
  
      Input is flattened if not already 1-dimensional.
  
      Args:
          x (Tensor): An N-D Tensor or a Scalar Tensor. 
          y (Tensor): An N-D Tensor or a Scalar Tensor. 
          name(str, optional): The default value is None. Normally there is no need for
              user to set this property. For more information, please refer to :ref:`api_guide_Name`
  
      Returns:
          Tensor: The outer-product Tensor.
  
      Examples:
          .. code-block:: python
  
              import paddle
              x = paddle.arange(1, 4).astype('float32')
              y = paddle.arange(1, 6).astype('float32')
              out = paddle.outer(x, y)
              print(out)
              #        ([[1, 2, 3, 4, 5],
              #         [2, 4, 6, 8, 10],
              #         [3, 6, 9, 12, 15]])
function: def outer(x, y, name=None):
      """
  
      Outer product of two Tensors.
  
      Input is flattened if not already 1-dimensional.
  
      Args:
          x (Tensor): An N-D Tensor or a Scalar Tensor. 
          y (Tensor): An N-D Tensor or a Scalar Tensor. 
          name(str, optional): The default value is None. Normally there is no need for
              user to set this property. For more information, please refer to :ref:`api_guide_Name`
  
      Returns:
          Tensor: The outer-product Tensor.
  
      Examples:
          .. code-block:: python
  
              import paddle
              x = paddle.arange(1, 4).astype('float32')
              y = paddle.arange(1, 6).astype('float32')
              out = paddle.outer(x, y)
              print(out)
              #        ([[1, 2, 3, 4, 5],
              #         [2, 4, 6, 8, 10],
              #         [3, 6, 9, 12, 15]])
  
  
      """
      nx = x.reshape((-1, 1))
      ny = y.reshape((1, -1))
  
      if in_dygraph_mode():
          return _C_ops.matmul_v2(nx, ny)
  
      def __check_input(x, y):
          var_names = {'x': x, 'y': y}
          for name, val in var_names.items():
              check_variable_and_dtype(val, name,
                                       ['float16', 'float32', 'float64'], 'inner')
  
      __check_input(nx, ny)
  
      helper = LayerHelper('outer', **locals())
      out = helper.create_variable_for_type_inference(dtype=nx.dtype)
      helper.append_op(
          type='matmul_v2', inputs={'X': nx,
                                 'Y': ny}, outputs={'Out': out})
      return out
function_tokens: def outer ( x , y , name = None ) : nx = x . reshape ( ( - 1 , 1 ) ) ny = y . reshape ( ( 1 , - 1 ) ) if in_dygraph_mode ( ) : return _C_ops . matmul_v2 ( nx , ny ) def __check_input ( x , y ) : var_names = { 'x' : x , 'y' : y } for name , val in var_names . items ( ) : check_variable_and_dtype ( val , name , [ 'float16' , 'float32' , 'float64' ] , 'inner' ) __check_input ( nx , ny ) helper = LayerHelper ( 'outer' , * * locals ( ) ) out = helper . create_variable_for_type_inference ( dtype = nx . dtype ) helper . append_op ( type = 'matmul_v2' , inputs = { 'X' : nx , 'Y' : ny } , outputs = { 'Out' : out } ) return out