code: def _get_superresolution_dummy_components(self):
      torch.manual_seed(0)
      text_encoder = T5EncoderModel.from_pretrained(
          "hf-internal-testing/tiny-random-t5"
      )
  
      torch.manual_seed(0)
      tokenizer = AutoTokenizer.from_pretrained(
          "hf-internal-testing/tiny-random-t5"
      )
  
      torch.manual_seed(0)
      unet = UNet2DConditionModel(
          sample_size=32,
          layers_per_block=[1, 2],
          block_out_channels=[32, 64],
          down_block_types=[
              "ResnetDownsampleBlock2D",
              "SimpleCrossAttnDownBlock2D",
          ],
          mid_block_type="UNetMidBlock2DSimpleCrossAttn",
          up_block_types=[
              "SimpleCrossAttnUpBlock2D",
              "ResnetUpsampleBlock2D",
          ],
          in_channels=6,
          out_channels=6,
          cross_attention_dim=32,
          encoder_hid_dim=32,
          attention_head_dim=8,
          addition_embed_type="text",
          addition_embed_type_num_heads=2,
          cross_attention_norm="group_norm",
          resnet_time_scale_shift="scale_shift",
          act_fn="gelu",
          class_embed_type="timestep",
          mid_block_scale_factor=1.414,
          time_embedding_act_fn="gelu",
          time_embedding_dim=32,
      )
      unet.set_attn_processor(
          AttnAddedKVProcessor()
      )  # For reproducibility tests
  
      torch.manual_seed(0)
      scheduler = DDPMScheduler(
          num_train_timesteps=1000,
          beta_schedule="squaredcos_cap_v2",
          beta_start=0.0001,
          beta_end=0.02,
          thresholding=True,
          dynamic_thresholding_ratio=0.95,
          sample_max_value=1.0,
          prediction_type="epsilon",
          variance_type="learned_range",
      )
  
      torch.manual_seed(0)
      image_noising_scheduler = DDPMScheduler(
          num_train_timesteps=1000,
          beta_schedule="squaredcos_cap_v2",
          beta_start=0.0001,
          beta_end=0.02,
      )
  
      torch.manual_seed(0)
      watermarker = IFWatermarker()
  
      return {
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "unet": unet,
          "scheduler": scheduler,
          "image_noising_scheduler": image_noising_scheduler,
          "watermarker": watermarker,
          "safety_checker": None,
          "feature_extractor": None,
      }
  
file_path: llmga/llmga/diffusers/tests/pipelines/deepfloyd_if/__init__.py
imports: import tempfile
  import numpy
  import torch
  from transformers import AutoTokenizer, T5EncoderModel
  from diffusers import DDPMScheduler, UNet2DConditionModel
  from diffusers.models.attention_processor import AttnAddedKVProcessor
  from diffusers.pipelines.deepfloyd_if import IFWatermarker
  from diffusers.utils.testing_utils import torch_device
  from ..test_pipelines_common import to_np
label_desc: Use image node_id 2 for calling the IFPipelineTesterMixin obj's underlying member method code with example usage: obj._get_superresolution_dummy_components() and returns: dict