class_docstr: When an object of this class enters a `with` block, it starts:
  - preventing torch's layer initialization functions from working
  - changes CLIP and OpenCLIP to not download model weights
  - changes CLIP to not make requests to check if there is a new version of a file you already have
  
  When it leaves the block, it reverts everything to how it was before.
  
  Use it like this:
  ```
  with DisableInitialization():
      do_things()
  ```
code: def __enter__(self):
      def do_nothing(*args, **kwargs):
          pass
  
      def create_model_and_transforms_without_pretrained(
          *args, pretrained=None, **kwargs
      ):
          return self.create_model_and_transforms(
              *args, pretrained=None, **kwargs
          )
  
      def CLIPTextModel_from_pretrained(
          pretrained_model_name_or_path, *model_args, **kwargs
      ):
          res = self.CLIPTextModel_from_pretrained(
              None,
              *model_args,
              config=pretrained_model_name_or_path,
              state_dict={},
              **kwargs,
          )
          res.name_or_path = pretrained_model_name_or_path
          return res
  
      def transformers_modeling_utils_load_pretrained_model(
          *args, **kwargs
      ):
          args = (
              args[0:3] + ("/",) + args[4:]
          )  # resolved_archive_file; must set it to something to prevent what seems to be a bug
          return self.transformers_modeling_utils_load_pretrained_model(
              *args, **kwargs
          )
  
      def transformers_utils_hub_get_file_from_cache(
          original, url, *args, **kwargs
      ):
          # this file is always 404, prevent making request
          if (
              url
              == "https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/added_tokens.json"
              or url == "openai/clip-vit-large-patch14"
              and args[0] == "added_tokens.json"
          ):
              return None
  
          try:
              res = original(
                  url, *args, local_files_only=True, **kwargs
              )
              if res is None:
                  res = original(
                      url, *args, local_files_only=False, **kwargs
                  )
              return res
          except Exception:
              return original(
                  url, *args, local_files_only=False, **kwargs
              )
  
      def transformers_utils_hub_get_from_cache(
          url, *args, local_files_only=False, **kwargs
      ):
          return transformers_utils_hub_get_file_from_cache(
              self.transformers_utils_hub_get_from_cache,
              url,
              *args,
              **kwargs,
          )
  
      def transformers_tokenization_utils_base_cached_file(
          url, *args, local_files_only=False, **kwargs
      ):
          return transformers_utils_hub_get_file_from_cache(
              self.transformers_tokenization_utils_base_cached_file,
              url,
              *args,
              **kwargs,
          )
  
      def transformers_configuration_utils_cached_file(
          url, *args, local_files_only=False, **kwargs
      ):
          return transformers_utils_hub_get_file_from_cache(
              self.transformers_configuration_utils_cached_file,
              url,
              *args,
              **kwargs,
          )
  
      self.replace(torch.nn.init, "kaiming_uniform_", do_nothing)
      self.replace(torch.nn.init, "_no_grad_normal_", do_nothing)
      self.replace(torch.nn.init, "_no_grad_uniform_", do_nothing)
  
      if self.disable_clip:
          self.create_model_and_transforms = self.replace(
              open_clip,
              "create_model_and_transforms",
              create_model_and_transforms_without_pretrained,
          )
          self.CLIPTextModel_from_pretrained = self.replace(
              ldm.modules.encoders.modules.CLIPTextModel,
              "from_pretrained",
              CLIPTextModel_from_pretrained,
          )
          self.transformers_modeling_utils_load_pretrained_model = (
              self.replace(
                  transformers.modeling_utils.PreTrainedModel,
                  "_load_pretrained_model",
                  transformers_modeling_utils_load_pretrained_model,
              )
          )
          self.transformers_tokenization_utils_base_cached_file = (
              self.replace(
                  transformers.tokenization_utils_base,
                  "cached_file",
                  transformers_tokenization_utils_base_cached_file,
              )
          )
          self.transformers_configuration_utils_cached_file = (
              self.replace(
                  transformers.configuration_utils,
                  "cached_file",
                  transformers_configuration_utils_cached_file,
              )
          )
          self.transformers_utils_hub_get_from_cache = self.replace(
              transformers.utils.hub,
              "get_from_cache",
              transformers_utils_hub_get_from_cache,
          )
  
imports: import ldm.modules.encoders.modules
  import open_clip
  import torch
  import transformers.utils.hub
  from modules import shared
label_desc: Use image node_id 3 for calling the DisableInitialization obj's underlying member method code with example usage: obj.__enter__() and returns: self, res, self, transformers_utils_hub_get_file_from_cache, transformers_utils_hub_get_file_from_cache, transformers_utils_hub_get_file_from_cache, None, res, original