class_docstr: Concatenate multiple tensors towards specified axis.
class_docstr_tok: ["Concatenate","multiple","tensors","towards","specified","axis","."]
code: def backward(self, indexes, grad_outputs):
      if len(self.inputs) == 1:
          return grad_outputs
  
      sizes = numpy.array(
          [v.shape[self.axis] for v in self.inputs[:-1]]
      ).cumsum()
      (gx,) = grad_outputs
      return chainer.functions.split_axis(gx, sizes, self.axis)
  
file_path: chainer/chainer/functions/array/concat.py
imports: import numpy
  import six
  import chainer
  from chainer import backend
  from chainer.backends import intel64
  from chainer import function_node
  from chainer.utils import type_check
  import chainerx
label_desc: Use image node_id 6 for calling the Concat obj's underlying member method code with example usage: obj.backward(indexes, grad_outputs) and returns: chainer, grad_outputs