code: def test_hific_generator_forward(
      latent_features: int, arange_4d_image: Tensor
  ):
      rng = Generator()
      latent_shape = (
          arange_4d_image.shape[0],
          latent_features,
          arange_4d_image.shape[2] // 16,
          arange_4d_image.shape[3] // 16,
      )
      latent = torch.randn(latent_shape, generator=rng)
      model = HiFiCGenerator(
          image_channels=arange_4d_image.shape[1],
          latent_features=latent_features,
      )
  
      output = model(latent)
  
      assert output.shape == arange_4d_image.shape
  
imports: import pytest
  import torch
  from torch import Generator, Tensor
  from neuralcompression.models._hific_encoder_decoder import HiFiCGenerator
label_desc: Use image node_id 1 for calling a global function with example usage: test_hific_generator_forward(latent_features, arange_4d_image) without return types