code: def dummy_text_encoder(self):
      torch.manual_seed(0)
      config = CLIPTextConfig(
          bos_token_id=0,
          eos_token_id=2,
          hidden_size=32,
          intermediate_size=37,
          layer_norm_eps=1e-05,
          num_attention_heads=4,
          num_hidden_layers=5,
          pad_token_id=1,
          vocab_size=1000,
          # SD2-specific config below
          hidden_act="gelu",
          projection_dim=64,
      )
      return CLIPTextModel(config)
  
imports: import gc
  import time
  import unittest
  import numpy
  import torch
  from huggingface_hub import hf_hub_download
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  from diffusers import AutoencoderKL, DDIMScheduler, DPMSolverMultistepScheduler, EulerDiscreteScheduler, StableDiffusionPipeline, UNet2DConditionModel
  from diffusers.models.attention_processor import AttnProcessor
  from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, numpy_cosine_similarity_distance, require_torch_gpu, slow, torch_device
label_desc: Use image node_id 4 for calling the StableDiffusion2VPredictionPipelineFastTests obj's underlying member method code with example usage: obj.dummy_text_encoder() and returns: CLIPTextModel