code: def generate_dummy_data(model: str = ""):
      """Generates dummy data for text and vision transformers.
      Args:
          model (str, optional): The name of the transformer model. Defaults to an empty string.
      """
      # For the vision models load an image and process it
      if model == "beit" or model == "clip" or model == "vit":
          url = "http://images.cocodataset.org/val2017/000000039769.jpg"
          image = Image.open(requests.get(url, stream=True).raw)
          if model == "beit":
              processor = BeitImageProcessor.from_pretrained(
                  "microsoft/beit-base-patch16-224-pt22k"
              )
          if model == "clip":
              processor = CLIPProcessor.from_pretrained(
                  "openai/clip-vit-base-patch32"
              )
          if model == "vit":
              processor = ViTImageProcessor.from_pretrained(
                  "google/vit-base-patch16-224-in21k"
              )
          return processor(images=image, return_tensors="pt")
  
      # For text just create and process a dummy string.
      else:
          input_ids = [i for i in range(20)]
          attention_mask = [1 for i in range(len(input_ids))]
          input_ids_tensor = torch.tensor([input_ids])
          attention_mask_tensor = torch.tensor([attention_mask])
          if model == "t5" or model == "encoder_decoder":
              return BatchEncoding(
                  {
                      "input_ids": input_ids_tensor,
                      "decoder_input_ids": input_ids_tensor,
                      "attention_mask": attention_mask_tensor,
                  }
              )
          return BatchEncoding(
              {
                  "input_ids": input_ids_tensor,
                  "attention_mask": attention_mask_tensor,
              }
          )
  
imports: import os
  import random
  from typing import Any, Union
  import numpy
  import torch
  from PIL import Image
  from torch import squeeze
  import jsonlines
  import requests
  import transformers
  from adapters import AutoAdapterModel, init
  from transformers import AlbertConfig, BartConfig, BatchEncoding, BeitConfig, BeitImageProcessor, BertConfig, CLIPProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection, DebertaConfig, DebertaV2Config, DistilBertConfig, EncoderDecoderConfig, EncoderDecoderModel, GPT2Config, GPTJConfig, MBartConfig, RobertaConfig, T5Config, ViTConfig, ViTImageProcessor, XLMRobertaConfig
label_desc: Use image node_id 7 for calling a global function with example usage: generate_dummy_data(model) and returns: processor, BatchEncoding, BatchEncoding