content: import torch
  
  
  def BertModule():
      from transformers import BertModel
  
      model_name = "bert-base-uncased"
      model = BertModel.from_pretrained(model_name, torchscript=True)
      model.eval()
      return model
  
  
  def BertInputs():
      from transformers import BertTokenizer
  
      model_name = "bert-base-uncased"
      enc = BertTokenizer.from_pretrained(model_name)
      text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
      tokenized_text = enc.tokenize(text)
      masked_index = 8
      tokenized_text[masked_index] = "[MASK]"
      indexed_tokens = enc.convert_tokens_to_ids(tokenized_text)
      segments_ids = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]
      tokens_tensor = torch.tensor([indexed_tokens])
      segments_tensors = torch.tensor([segments_ids])
      return [tokens_tensor, segments_tensors]
  
  
  def StableDiffusion1_4_Unet():
      from diffusers import DiffusionPipeline
  
      pipe = DiffusionPipeline.from_pretrained(
          "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16
      )
      return pipe.unet
  
  
  def StableDiffusion2_1_Unet():
      from diffusers import StableDiffusionPipeline
  
      pipe = StableDiffusionPipeline.from_pretrained(
          "stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16
      )
      return pipe.unet
  
  
  def StableDiffusion2_1_VaeDecoder():
      from diffusers import StableDiffusionPipeline
  
      pipe = StableDiffusionPipeline.from_pretrained(
          "stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16
      )
      return pipe.vae.decoder
  
  
  def MonaiUNet():
      from monai.networks.nets import UNet
  
      model = UNet(
          spatial_dims=2,
          in_channels=32,
          out_channels=32,
          channels=(4, 8, 16),
          strides=(2, 2),
          num_res_units=2,
      )
      return model
  
  
  def GoogleViTForImageClassification():
      from transformers import ViTForImageClassification
  
      model = ViTForImageClassification.from_pretrained(
          "google/vit-base-patch16-224", torch_dtype=torch.float16
      )
      return model
  