code: def transpose_data_grad_backward(
      grad_inputs, inputs, input_shapes, outputs, output_shapes, axes
  ):
      """
      Args:
        grad_inputs (list of :obj:`nnabla.Variable`): Propagated grads to this backward function.
        inputs (list of :obj:`nnabla.Variable` and None): Input Variables of the forward function
            if this backward function depends on it. Otherwise, None is set instead.
        input_shapes (list of tuple of :obj:`int`): Input shapes of the forward function.
            The shapes of the inputs in which None is set can be passed.
        outputs (list of :obj:`nnabla.Variable` and None): Output Variables of the forward function
            if this backward function depends on it. Otherwise, None is set instead.
        output_shapes (list of tuple of :obj:`int`): Output shapes of the forward function.
            The shapes of the outputs in which None is set can be passed.
        kwargs (dict of arguments): Dictionary of the corresponding function arguments.
  
      Return:
        list of Variable: Return the gradients wrt inputs of the corresponding function.
      """
      gdx = grad_inputs[0]
      gdy = F.transpose(gdx, axes)
      return gdy
  
file_path: nnabla/python/src/nnabla/backward_function/transpose.py
imports: import nnabla
  import nnabla.function
  import nnabla.functions
  from .backward_function import UnaryDataGrad
label_desc: Use image node_id 2 for calling a global function with example usage: transpose_data_grad_backward(grad_inputs, inputs, input_shapes, outputs, output_shapes, axes) and returns: gdy