code: def test_contrastive_loss_in_out(
      batch_size: int, projection_dim: int
  ) -> None:
      # These tests check the input and output formats are correct or not.
      parser = argparse.ArgumentParser()
      parser = ContrastiveLossClip.add_arguments(parser)
  
      opts = parser.parse_args([])
      criteria = ContrastiveLossClip(opts)
  
      image_features = torch.randn(size=(batch_size, projection_dim))
      text_features = torch.randn(size=(batch_size, projection_dim))
  
      input_sample = None
      targets = None
  
      prediction = {"image": image_features, "text": text_features}
  
      loss_output = criteria(input_sample, prediction, targets)
      expected_output_keys = {
          "total_loss",
          "image_loss",
          "text_loss",
          "logit_scale",
      }
      assert expected_output_keys.issubset(loss_output.keys())
  
      for loss_name, loss_val in loss_output.items():
          if loss_name == "logit_scale" and isinstance(
              loss_val, (float, int)
          ):
              loss_val = torch.tensor(loss_val)
          assert isinstance(
              loss_val, torch.Tensor
          ), "Loss should be an instance of torch.Tensor"
          assert loss_val.dim() == 0, "Loss value should be a scalar"
  
imports: import argparse
  import pytest
  import torch
  from loss_fn.multi_modal_img_text.contrastive_loss_clip import ContrastiveLossClip
label_desc: Use image node_id 1 for calling a global function with example usage: test_contrastive_loss_in_out(batch_size, projection_dim) without return types