code: def test_model(config_file: str, batch_size: int):
      opts = get_config(config_file=config_file)
      setattr(opts, "common.debug_mode", True)
  
      # removing pretrained models (if any) for now to reduce test time as well as access issues
      unset_pretrained_models_from_opts(opts)
  
      model = get_model(opts)
  
      criteria = build_loss_fn(opts)
  
      inputs = None
      targets = None
      if hasattr(model, "dummy_input_and_label"):
          inputs_and_targets = model.dummy_input_and_label(batch_size)
          inputs = inputs_and_targets["samples"]
          targets = inputs_and_targets["targets"]
  
      assert inputs is not None, (
          "Input tensor can't be None. This is likely because "
          "{} does not implement dummy_input_and_label function".format(
              model.__class__.__name__
          )
      )
      assert targets is not None, (
          "Label tensor can't be None. This is likely because "
          "{} does not implement dummy_input_and_label function".format(
              model.__class__.__name__
          )
      )
  
      # if getattr(opts, "common.channels_last", False):
      #    inputs = inputs.to(memory_format=torch.channels_last)
      #    model = model.to(memory_format=torch.channels_last)
  
      try:
          outputs = model(inputs)
  
          loss = criteria(
              input_sample=inputs,
              prediction=outputs,
              target=targets,
              epoch=0,
              iterations=0,
          )
  
          print(f"Loss: {loss}")
  
          if isinstance(loss, Tensor):
              loss.backward()
          elif isinstance(loss, Dict):
              loss["total_loss"].backward()
          else:
              raise RuntimeError(
                  "The output of criteria should be either Dict or Tensor"
              )
  
          # If there are unused parameters in gradient computation, print them
          # This may be useful for debugging purposes
          unused_params = []
          for name, param in model.named_parameters():
              if param.grad is None:
                  unused_params.append(name)
          if len(unused_params) > 0:
              print("Unused parameters: {}".format(unused_params))
  
      except Exception as e:
          if (
              isinstance(e, ValueError)
              and str(e).find(
                  "Expected more than 1 value per channel when training"
              )
              > -1
              and batch_size == 1
          ):
              # For segmentation models (e.g., PSPNet), we pool the tensor so that they have a spatial size of 1.
              # In such a case, batch norm needs a batch size > 1. Otherwise, we can't compute the statistics, raising
              # ValueError("Expected more than 1 value per channel when training"). If we encounter this error
              # for a batch size of 1, we skip it.
              pytest.skip(str(e))
          else:
              raise e
  
file_path: ml-cvnets/tests/test_model.py
imports: import sys
  from pathlib import Path
  import pytest
  from typing import Dict
  from torch import Tensor
  from cvnets import get_model
  from loss_fn import build_loss_fn
  from tests.configs import get_config
  from tests.test_utils import unset_pretrained_models_from_opts
label_desc: Use image node_id 1 for calling a global function with example usage: test_model(config_file, batch_size) without return types