code: def get_tokenizer(
      tokenizer_loader,
      tokenizer_base_model,
      local_files_only,
      resume_download,
      use_auth_token,
  ):
      tokenizer = tokenizer_loader.from_pretrained(
          tokenizer_base_model,
          local_files_only=local_files_only,
          resume_download=resume_download,
          token=use_auth_token,
          padding_side="left",
      )
  
      tokenizer.pad_token_id = 0  # different from the eos token
      # when generating, we will use the logits of right-most token to predict the next token
      # so the padding should be on the left,
      # e.g. see: https://huggingface.co/transformers/v4.11.3/model_doc/t5.html#inference
      tokenizer.padding_side = "left"  # Allow batched inference
  
      return tokenizer
  
imports: import functools
  import json
  from src.enums import t5_type
  from src.utils import have_optimum
label_desc: Use image node_id 2 for calling a global function with example usage: get_tokenizer(tokenizer_loader, tokenizer_base_model, local_files_only, resume_download, use_auth_token) and returns: tokenizer