code: def get_dummy_inputs(self, device, seed=0):
      if str(device).startswith("mps"):
          generator = torch.manual_seed(seed)
      else:
          generator = torch.Generator(device=device).manual_seed(seed)
      inputs = {
          "class_labels": [1],
          "generator": generator,
          "num_inference_steps": 2,
          "output_type": "numpy",
      }
      return inputs
  
imports: import gc
  import unittest
  import numpy
  import torch
  from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, Transformer2DModel
  from diffusers.utils import is_xformers_available
  from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, nightly, require_torch_gpu, torch_device
  from ..pipeline_params import CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS, CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin
label_desc: Use image node_id 2 for calling the DiTPipelineFastTests obj's underlying member method code with example usage: obj.get_dummy_inputs(device, seed) and returns: inputs