code: def test_condunet_discriminator(
      shape,
      context_shape,
      norm_type,
      output_channels,
      downsampling_factor,
  ):
      image = unet_image(shape)
      context = unet_image(context_shape)
      model = ConditionalUnetDiscriminator(
          output_downsampling_factor=downsampling_factor,
          output_channels=output_channels,
          context_in=context.shape[1],
          norm_type=norm_type,
      )
  
      expected_shape = (
          shape[0],
          output_channels,
          shape[2] // downsampling_factor,
          shape[3] // downsampling_factor,
      )
  
      output = model(image, context)
  
      assert output.shape == expected_shape
  
imports: import pytest
  import torch
  import torch.nn
  from neuralcompression.models import ConditionalUnetDiscriminator, UnetDiscriminator
label_desc: Use image node_id 3 for calling a global function with example usage: test_condunet_discriminator(shape, context_shape, norm_type, output_channels, downsampling_factor) without return types