class_docstr: Automatic differentiation given an op-tree.
  
  Arguments:
      op_tree (OpTreeNode): the op-tree to take gradient of
      be (Backend): computation backend used
      next_error (Tensor or OpTreeNode, optional): next layer's error, usually
                                                   self.delta in a layer. If
                                                   set to None, then automatically
                                                   the default value is tensor
                                                   ones() in output shape
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docstr: Get gradient values as numpy array w.r.t the list of `tensors`. If a
  tensor is not used, its gradient will be set to zero.
  
  Arguments:
      Tensors (list): List of Tensors to compute gradients.
  
  Returns
      list: A list of numpy.ndarray, each of them is the gradient of the
            input tensor.
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code: def get_grad_asnumpyarray(self, tensors):
      """
      Get gradient values as numpy array w.r.t the list of `tensors`. If a
      tensor is not used, its gradient will be set to zero.
  
      Arguments:
          Tensors (list): List of Tensors to compute gradients.
  
      Returns
          list: A list of numpy.ndarray, each of them is the gradient of the
                input tensor.
      """
      grad_vals = self.get_grad_tensor(tensors)
      for i in range(len(grad_vals)):
          grad_vals[i] = grad_vals[i].get().astype(self.dtype)
      return grad_vals
  
imports: from __future__ import division
  from builtins import object, zip
  from neon.backends.backend import OpTreeNode, Tensor
  import numpy
  from functools import wraps
label_desc: Use image node_id 7 for calling the Autodiff obj's underlying member method code with example usage: obj.get_grad_asnumpyarray(tensors) and returns: grad_vals