code: def test_seg_cross_entropy_in_out(
      batch_size: int,
      label_smoothing: float,
      ignore_index: int,
      class_weights: bool,
      num_classes: int,
      aux_weight: float,
  ) -> None:
      # These tests check the input and output formats are correct or not.
  
      # build configuration
      parser = argparse.ArgumentParser()
      parser = SegCrossEntropy.add_arguments(parser)
  
      opts = parser.parse_args([])
  
      setattr(
          opts,
          "loss.segmentation.cross_entropy.label_smoothing",
          label_smoothing,
      )
      setattr(
          opts,
          "loss.segmentation.cross_entropy.ignore_index",
          ignore_index,
      )
      setattr(
          opts,
          "loss.segmentation.cross_entropy.class_weights",
          class_weights,
      )
      setattr(
          opts, "loss.segmentation.cross_entropy.aux_weight", aux_weight
      )
  
      criteria = SegCrossEntropy(opts)
      height = 10
      width = 10
  
      # Four prediction cases:
      # Case 1: Prediction is a tensor
      # Case 2: Prediction is a dictionary, with segmentation_output as a mandatory key
      # Case 3: Prediction is a Tuple[tensor, tensor]
      # Case 4: Prediction is a dictionary, with segmentation_output as a mandatory key and is a Tuple[Tensor, Tensor]
      pred_case_1 = torch.randn(
          size=(batch_size, num_classes, height, width)
      )
      pred_case_2 = {"segmentation_output": pred_case_1}
      pred_case_3 = (
          torch.randn(size=(batch_size, num_classes, height, width)),
          torch.randn(size=(batch_size, num_classes, height, width)),
      )
      pred_case_4 = {"segmentation_output": pred_case_3}
  
      target = torch.randint(
          low=0, high=num_classes, size=(batch_size, height, width)
      )
      # randomly set indices in target tensor to ignore_index
      random_indices = (torch.rand_like(target.float()) > 0.5) * 1.0
      random_indices = random_indices.to(dtype=torch.int)
      target[random_indices == 0] = ignore_index
  
      # this loss function does not care about it, so we can have any input
      input_sample = torch.randint(low=0, high=1, size=(1,))
      for pred in [pred_case_1, pred_case_2, pred_case_3, pred_case_4]:
          loss = criteria(input_sample, pred, target)
          assert isinstance(
              loss, dict
          ), "loss should be an instance of dict"
          assert (
              "total_loss" in loss
          ), "total_loss is a mandatory key in loss"
          if len(loss) == 3:
              # when we compute aux loss, in that case, seg_loss and aux_loss are also returned
              assert {"total_loss", "seg_loss", "aux_loss"}.issubset(
                  loss.keys()
              )
          for loss_key, loss_val in loss.items():
              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.segmentation.cross_entropy import SegCrossEntropy
label_desc: Use image node_id 1 for calling a global function with example usage: test_seg_cross_entropy_in_out(batch_size, label_smoothing, ignore_index, class_weights, num_classes, aux_weight) without return types