code: def test_inference_batch_single_identical(
      self,
      batch_size=3,
      test_max_difference=None,
      test_mean_pixel_difference=None,
      relax_max_difference=False,
      expected_max_diff=2e-3,
      additional_params_copy_to_batched_inputs=["num_inference_steps"],
  ):
      if test_max_difference is None:
          # TODO(Pedro) - not sure why, but not at all reproducible at the moment it seems
          # make sure that batched and non-batched is identical
          test_max_difference = torch_device != "mps"
  
      if test_mean_pixel_difference is None:
          # TODO same as above
          test_mean_pixel_difference = torch_device != "mps"
  
      components = self.get_dummy_components()
      pipe = self.pipeline_class(**components)
      pipe.to(torch_device)
      pipe.set_progress_bar_config(disable=None)
  
      inputs = self.get_dummy_inputs(torch_device)
  
      logger = logging.get_logger(pipe.__module__)
      logger.setLevel(level=diffusers.logging.FATAL)
  
      # batchify inputs
      batched_inputs = {}
      batch_size = batch_size
      for name, value in inputs.items():
          if name in self.batch_params:
              # prompt is string
              if name == "prompt":
                  len_prompt = len(value)
                  # make unequal batch sizes
                  batched_inputs[name] = [
                      value[: len_prompt // i]
                      for i in range(1, batch_size + 1)
                  ]
  
                  # make last batch super long
                  batched_inputs[name][-1] = 100 * "very long"
              elif name == "image":
                  batched_images = []
  
                  for image in value:
                      batched_images.append(batch_size * [image])
  
                  batched_inputs[name] = batched_images
              else:
                  batched_inputs[name] = batch_size * [value]
          elif name == "batch_size":
              batched_inputs[name] = batch_size
          elif name == "generator":
              batched_inputs[name] = [
                  self.get_generator(i) for i in range(batch_size)
              ]
          else:
              batched_inputs[name] = value
  
      for arg in additional_params_copy_to_batched_inputs:
          batched_inputs[arg] = inputs[arg]
  
      if self.pipeline_class.__name__ != "DanceDiffusionPipeline":
          batched_inputs["output_type"] = "np"
  
      output_batch = pipe(**batched_inputs)
      assert output_batch[0].shape[0] == batch_size
  
      inputs["generator"] = self.get_generator(0)
  
      output = pipe(**inputs)
  
      logger.setLevel(level=diffusers.logging.WARNING)
      if test_max_difference:
          if relax_max_difference:
              # Taking the median of the largest <n> differences
              # is resilient to outliers
              diff = np.abs(output_batch[0][0] - output[0][0])
              diff = diff.flatten()
              diff.sort()
              max_diff = np.median(diff[-5:])
          else:
              max_diff = np.abs(output_batch[0][0] - output[0][0]).max()
          assert max_diff < expected_max_diff
  
      if test_mean_pixel_difference:
          assert_mean_pixel_difference(output_batch[0][0], output[0][0])
  
imports: import gc
  import random
  import unittest
  import numpy
  import torch
  from parameterized import parameterized
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  import diffusers
  from diffusers import AutoencoderKL, LCMScheduler, MultiAdapter, PNDMScheduler, StableDiffusionAdapterPipeline, T2IAdapter, UNet2DConditionModel
  from diffusers.utils import logging
  from diffusers.utils.import_utils import is_xformers_available
  from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, load_image, load_numpy, numpy_cosine_similarity_distance, require_torch_gpu, slow, torch_device
  from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
label_desc: Use image node_id 7 for calling the StableDiffusionMultiAdapterPipelineFastTests obj's underlying member method code with example usage: obj.test_inference_batch_single_identical(batch_size, test_max_difference, test_mean_pixel_difference, relax_max_difference, expected_max_diff, additional_params_copy_to_batched_inputs) without return types