code: def test_neural_aug_loss_in_out(batch_size: int) -> None:
      # These tests check the input and output formats are correct or not.
      # build configuration
      parser = argparse.ArgumentParser()
      parser = NeuralAugmentation.add_arguments(parser)
  
      opts = parser.parse_args([])
      setattr(opts, "scheduler.max_epochs", 20)
  
      # build loss function
      neural_aug_loss_fn = NeuralAugmentation(opts)
      pred_tensor = {
          "augmented_tensor": torch.zeros(
              size=(batch_size, 3, 224, 224), dtype=torch.float
          )
      }
  
      # Three input cases:
      # Case 1: Input image is a tensor
      # Case 2: Input is a dictionary, with image as a mandatory key and value as a batch of input image tensor
      # Case 3: Input is a dictionary, with image as a mandatory key and value as a list of input image tensor
      input_case_1 = torch.randint(
          low=0, high=1, size=(batch_size, 3, 224, 224)
      )
      input_case_2 = {
          "image": torch.randint(
              low=0, high=1, size=(batch_size, 3, 224, 224)
          )
      }
      input_case_3 = {
          "image": [torch.randint(low=0, high=1, size=(1, 3, 224, 224))]
          * batch_size
      }
  
      for inp in [input_case_1, input_case_2, input_case_3]:
          loss = neural_aug_loss_fn(inp, pred_tensor)
          assert isinstance(
              loss, torch.Tensor
          ), "Loss should be an instance of torch.Tensor"
          assert loss.dim() == 0, "Loss value should be a scalar"
  
imports: import argparse
  import pytest
  import torch
  from loss_fn.neural_augmentation import NeuralAugmentation
label_desc: Use image node_id 1 for calling a global function with example usage: test_neural_aug_loss_in_out(batch_size) without return types