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 = {
          "batch_size": 1,
          "num_inference_steps": None,
          "timesteps": [22, 0],
          "generator": generator,
          "output_type": "np",
      }
  
      return inputs
  
imports: import gc
  import unittest
  import numpy
  import torch
  from torch.backends.cuda import sdp_kernel
  from diffusers import CMStochasticIterativeScheduler, ConsistencyModelPipeline, UNet2DModel
  from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_2, require_torch_gpu, torch_device
  from diffusers.utils.torch_utils import randn_tensor
  from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin
label_desc: Use image node_id 4 for calling the ConsistencyModelPipelineFastTests obj's underlying member method code with example usage: obj.get_dummy_inputs(device, seed) and returns: inputs