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 = inputs = {
          "prompt": "a cat and a frog",
          "token_indices": [2, 5],
          "generator": generator,
          "num_inference_steps": 1,
          "guidance_scale": 6.0,
          "output_type": "numpy",
          "max_iter_to_alter": 2,
          "thresholds": {0: 0.7},
      }
      return inputs
  
imports: import gc
  import unittest
  import numpy
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionAttendAndExcitePipeline, UNet2DConditionModel
  from diffusers.utils.testing_utils import load_numpy, nightly, numpy_cosine_similarity_distance, require_torch_gpu, skip_mps
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
label_desc: Use image node_id 4 for calling the StableDiffusionAttendAndExcitePipelineFastTests obj's underlying member method code with example usage: obj.get_dummy_inputs(device, seed) and returns: inputs