code: def test_inference_batch_consistent(
      self,
      batch_sizes=[2, 4, 13],
      additional_params_copy_to_batched_inputs=["num_inference_steps"],
  ):
      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
      for batch_size in batch_sizes:
          batched_inputs = {}
          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
              else:
                  batched_inputs[name] = value
  
          for arg in additional_params_copy_to_batched_inputs:
              batched_inputs[arg] = inputs[arg]
  
          batched_inputs["output_type"] = "np"
  
          if self.pipeline_class.__name__ == "DanceDiffusionPipeline":
              batched_inputs.pop("output_type")
  
          output = pipe(**batched_inputs)
  
          assert len(output[0]) == batch_size
  
          batched_inputs["output_type"] = "np"
  
          if self.pipeline_class.__name__ == "DanceDiffusionPipeline":
              batched_inputs.pop("output_type")
  
          output = pipe(**batched_inputs)[0]
  
          assert output.shape[0] == batch_size
  
      logger.setLevel(level=diffusers.logging.WARNING)
  
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 5 for calling the StableDiffusionMultiAdapterPipelineFastTests obj's underlying member method code with example usage: obj.test_inference_batch_consistent(batch_sizes, additional_params_copy_to_batched_inputs) without return types