class_docstr: Dict of nodes to references of gradients
  
  The gradients are stored as references to them in the backprop process. The
  current implementation uses lists. Keep the lengths of lists <= 1 for the
  strict accumulation of gradients. Leave them to accumulate gradients
  lazily.
  
  Args:
      accumulate_grad_inputs (bool): Fallback to grad_var of input variables.
          However, the current implementation reproduces the legacy behavior,
          i.e. to read ``grad_var`` of node when the node has not been added.
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code: def pop(self, node):
      if node is None:
          return None
      grads = self.grads
      if node in grads:
          return _reduce(grads.pop(node))
      if self._load_if_new:
          return _grad_var_from_alive_node(node)
      else:
          return None
  
imports: import os
  import shutil
  import sys
  import traceback
  import six
  import chainer
label_desc: Use image node_id 5 for calling the GradTable obj's underlying member method code with example usage: obj.pop(node) and returns: None, _reduce, _grad_var_from_alive_node, None