parameters: (self)
return_statement: return self.container
docstring: Get the tensors stored by ``save_for_backward``.
  
          Returns:
              list of Tensors or None: If context contains tensors stored by `save_for_backward`, 
              then return these tensors, otherwise return None.
  
          Examples:
              .. code-block:: python
  
                  import paddle
                  from paddle.autograd import PyLayer
  
                  class cus_tanh(PyLayer):
                      @staticmethod
                      def forward(ctx, x):
                          # ctx is a context object that store some objects for backward.
                          y = paddle.tanh(x)
                          # Pass tensors to backward.
                          ctx.save_for_backward(y)
                          return y
  
                      @staticmethod
                      def backward(ctx, dy):
                          # Get the tensors passed by forward.
                          y, = ctx.saved_tensor()
                          grad = dy * (1 - paddle.square(y))
                          return grad
docstring_summary: Get the tensors stored by ``save_for_backward``.
docstring_tokens: Get the tensors stored by save_for_backward .
function: def saved_tensor(self):
          """
          Get the tensors stored by ``save_for_backward``.
  
          Returns:
              list of Tensors or None: If context contains tensors stored by `save_for_backward`, 
              then return these tensors, otherwise return None.
  
          Examples:
              .. code-block:: python
  
                  import paddle
                  from paddle.autograd import PyLayer
  
                  class cus_tanh(PyLayer):
                      @staticmethod
                      def forward(ctx, x):
                          # ctx is a context object that store some objects for backward.
                          y = paddle.tanh(x)
                          # Pass tensors to backward.
                          ctx.save_for_backward(y)
                          return y
  
                      @staticmethod
                      def backward(ctx, dy):
                          # Get the tensors passed by forward.
                          y, = ctx.saved_tensor()
                          grad = dy * (1 - paddle.square(y))
                          return grad
          """
  
          return self.container
function_tokens: def saved_tensor ( self ) : return self . container