class_docstr: Padding arrays to create a matrix.
class_docstr_tok: ["Padding","arrays","to","create","a","matrix","."]
code: def backward(self, indexes, grad_outputs):
      (gy,) = grad_outputs
      inputs = self.inputs
      if gy.size == 0:
          # `split` in NumPy 1.9 behaves inconsistently when size is zero.
          gy = [gy]
      else:
          gy = chainer.functions.split_axis(gy, len(inputs), axis=0)
      return tuple(
          g[0, : x.shape[0]] for g, x in six.moves.zip(gy, inputs)
      )
  
file_path: chainer/chainer/functions/array/pad_sequence.py
imports: import numpy
  import six
  import chainer
  from chainer import backend
  from chainer.backends import cuda
  from chainer import function_node
  from chainer import utils
  from chainer.utils import type_check
label_desc: Use image node_id 4 for calling the PadSequence obj's underlying member method code with example usage: obj.backward(indexes, grad_outputs) and returns: tuple