code: def create_inputs(
      rng,
      kernel,
      pad,
      stride,
      dilation,
      group,
      with_bias,
      bn,
      add2,
      channel_last,
  ):
      from refs import get_conv_out_size
  
      bs = 2
      hw = (5, 5)
      total_pad = 2 * pad[0] if len(pad) == 1 else pad[0] + pad[1]
      pad_hw = tuple(s + total_pad for s in hw)
      ohw = tuple(
          get_conv_out_size(s, kernel, 0, stride, dilation)
          for s in pad_hw
      )
      ichannels = 4
      ochannels = 6
      kernels = (kernel, kernel)
      inshape = (bs, ichannels) + hw
      outshape = (bs, ochannels) + ohw
      wshape = (
          ochannels,
          ichannels // group,
      ) + kernels
  
      def to_channel_last(shape):
          return tuple(shape[i] for i in (0, 2, 3, 1))
  
      if channel_last:
          inshape = to_channel_last(inshape)
          outshape = to_channel_last(outshape)
          wshape = to_channel_last(wshape)
      x = rng.randn(*inshape).astype(np.float32)
      weight = rng.randn(*wshape).astype(np.float32)
      bias = None
      if with_bias:
          bias = rng.randn(ochannels).astype(np.float32)
      if bn is not None:
          axis = 3 if channel_last else 1
          shape_stat = [1 for _ in range(x.ndim)]
          shape_stat[axis] = ochannels
          beta = rng.randn(*shape_stat).astype(np.float32)
          gamma = rng.randn(*shape_stat).astype(np.float32)
          rmean = np.zeros(shape_stat, dtype=np.float32)
          rvar = np.ones(shape_stat, dtype=np.float32)
      else:
          beta = None
          gamma = None
          rmean = None
          rvar = None
      if add2:
          # Note: The last dimension must be a multiple of 4
          # if we want to test cudnn BN persistent mode.
          z = rng.randn(*outshape).astype(np.float32)
      else:
          z = None
  
      return x, weight, bias, beta, gamma, rmean, rvar, z
  
file_path: nnabla/python/test/function/test_fused_convolution.py
imports: import pytest
  import numpy
  import nnabla
  import nnabla.functions
  import nnabla.ext_utils
  from nbla_test_utils import list_context
label_desc: Use image node_id 4 for calling a global function with example usage: create_inputs(rng, kernel, pad, stride, dilation, group, with_bias, bn, add2, channel_last) and returns: x,  weight,  bias,  beta,  gamma,  rmean,  rvar,  z, tuple