code: def make_shape_cases(
      models,
      spatial_dims,
      batches,
      pretrained,
      in_channels=3,
      num_classes=1000,
      norm=("batch", {"eps": 1e-3, "momentum": 0.01}),
  ):
      ret_tests = []
      for spatial_dim in spatial_dims:  # selected spatial_dims
          for (
              batch
          ) in (
              batches
          ):  # check single batch as well as multiple batch input
              for model in models:  # selected models
                  for (
                      is_pretrained
                  ) in pretrained:  # pretrained or not pretrained
                      kwargs = {
                          "model_name": model,
                          "pretrained": is_pretrained,
                          "progress": False,
                          "spatial_dims": spatial_dim,
                          "in_channels": in_channels,
                          "num_classes": num_classes,
                          "norm": norm,
                      }
                      ret_tests.append(
                          [
                              kwargs,
                              (batch, in_channels)
                              + (get_expected_model_shape(model),)
                              * spatial_dim,
                              (batch, num_classes),
                          ]
                      )
      return ret_tests
  
imports: from __future__ import annotations
  import os
  import unittest
  from typing import TYPE_CHECKING
  from unittest import skipUnless
  import torch
  from parameterized import parameterized
  from monai.networks import eval_mode
  from monai.networks.nets import BlockArgs, EfficientNetBN, EfficientNetBNFeatures, drop_connect, get_efficientnet_image_size
  from monai.utils import optional_import
  from tests.utils import skip_if_downloading_fails, skip_if_quick, test_pretrained_networks, test_script_save
label_desc: Use image node_id 4 for calling a global function with example usage: make_shape_cases(models, spatial_dims, batches, pretrained, in_channels, num_classes, norm) and returns: ret_tests