code: def batch_normalization_backward(
      grad_inputs,
      inputs,
      input_shapes,
      outputs,
      output_shapes,
      axes=(1,),
      decay_rate=0.9,
      eps=1e-05,
      batch_stat=True,
      no_scale=False,
      no_bias=False,
  ):
      """
      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.
      """
      # In auto-forward mode, the dynamic clear of the all inputs are
      # blocked by BatchNormalization::auto_grad_depends_input/output_data.
      ctx = nn.get_current_context()
      x_ndim = len(input_shapes[0])
      axes = [a + x_ndim * (a < 0) for a in axes]
      df = BatchNormalizationBackward(
          ctx, axes, decay_rate, eps, batch_stat, no_scale, no_bias
      )
      d_inputs = df(*grad_inputs, *inputs)
      return force_tuple(d_inputs) + (None, None)
  
imports: from functools import partial
  import nnabla
  import nnabla.function
  import nnabla.functions
  import numpy
  from nnabla.function import PythonFunction
  from .utils import force_tuple
label_desc: Use image node_id 3 for calling a global function with example usage: batch_normalization_backward(grad_inputs, inputs, input_shapes, outputs, output_shapes, axes, decay_rate, eps, batch_stat, no_scale, no_bias) and returns: unknown