code: def test_preprocess_input_mask_3d_batch(self):
      image_processor = VaeImageProcessor(
          do_resize=False, do_normalize=False, do_convert_grayscale=True
      )
  
      # create a dummy mask input with batch_size 2
      dummy_mask_batch = torch.cat([self.dummy_mask] * 2, axis=0)
  
      # squeeze out the channel dimension
      input_pt_3d = dummy_mask_batch.squeeze(1)
      input_np_3d = self.to_np(dummy_mask_batch).squeeze(-1)
  
      input_pt_3d_list = list(input_pt_3d)
      input_np_3d_list = list(input_np_3d)
  
      out_pt_3d = image_processor.postprocess(
          image_processor.preprocess(input_pt_3d),
          output_type="np",
      )
      out_pt_3d_list = image_processor.postprocess(
          image_processor.preprocess(input_pt_3d_list),
          output_type="np",
      )
  
      assert np.abs(out_pt_3d - out_pt_3d_list).max() < 1e-6
  
      out_np_3d = image_processor.postprocess(
          image_processor.preprocess(input_np_3d),
          output_type="np",
      )
      out_np_3d_list = image_processor.postprocess(
          image_processor.preprocess(input_np_3d_list),
          output_type="np",
      )
  
      assert np.abs(out_np_3d - out_np_3d_list).max() < 1e-6
  
file_path: llmga/llmga/diffusers/tests/others/test_image_processor.py
imports: import unittest
  import numpy
  import PIL.Image
  import torch
  from diffusers.image_processor import VaeImageProcessor
label_desc: Use image node_id 11 for calling the ImageProcessorTest obj's underlying member method code with example usage: obj.test_preprocess_input_mask_3d_batch() without return types