code: def create_inputs(
      rng,
      x_shape,
      batch_axis,
      channel_axis,
      no_scale,
      no_bias,
      broadcast_affine_params,
  ):
      x = np.array(rng.randn(*x_shape).astype(np.float32))
  
      channel_axis += len(x_shape) * (channel_axis < 0)
  
      if broadcast_affine_params:
          affine_param_shape = tuple(
              [
                  x_shape[i]
                  if i
                  in [
                      channel_axis,
                  ]
                  else 1
                  for i in range(len(x_shape))
              ]
          )
      else:
          batch_axis = _force_list(batch_axis)
          batch_axis = [i + len(x_shape) * (i < 0) for i in batch_axis]
          affine_param_shape = tuple(
              [
                  x_shape[i]
                  if i
                  in batch_axis
                  + [
                      channel_axis,
                  ]
                  else 1
                  for i in range(len(x_shape))
              ]
          )
  
      beta = (
          None
          if no_bias
          else rng.randn(*affine_param_shape).astype(np.float32)
      )
      gamma = (
          None
          if no_scale
          else rng.randn(*affine_param_shape).astype(np.float32)
      )
  
      return x, beta, gamma
  
file_path: nnabla/python/test/function/test_instance_normalization.py
imports: import pytest
  import numpy
  import nnabla
  import nnabla.functions
  from nbla_test_utils import list_context
  from nnabla.normalization_functions import _force_list, _get_axes_excluding
label_desc: Use image node_id 2 for calling a global function with example usage: create_inputs(rng, x_shape, batch_axis, channel_axis, no_scale, no_bias, broadcast_affine_params) and returns: x,  beta,  gamma