code: def test_batch_normalization_for_multiple_axes_forward_backward(
      seed, axes, decay_rate, eps, output_stat, ctx, func_name
  ):
      rng = np.random.RandomState(seed)
      inputs = list(create_inputs_for_multiple_axes(rng, axes))
      vinputs = []
      for i in inputs:
          vinputs.append(nn.Variable(i.shape, True))
          vinputs[-1].d = i
  
      # Check if global stat mode works
      batch_stat = False
      if output_stat:
          return
      ref_y = ref_batch_normalization_for_multiple_axes(
          *(inputs + [axes, decay_rate, eps, batch_stat, output_stat])
      )
      with nn.context_scope(ctx), nn.auto_forward():
          y = F.batch_normalization(
              *(
                  vinputs
                  + [axes, decay_rate, eps, batch_stat, output_stat]
              )
          )
      assert_allclose(ref_y, y.d, atol=1e-6)
  
file_path: nnabla/python/test/function/test_batch_normalization.py
imports: from six.moves import range
  import pytest
  import numpy
  import nnabla
  import nnabla.functions
  from nbla_test_utils import list_context
  from nnabla.testing import assert_allclose
label_desc: Use image node_id 8 for calling a global function with example usage: test_batch_normalization_for_multiple_axes_forward_backward(seed, axes, decay_rate, eps, output_stat, ctx, func_name) without return types