code: def build_t5(opt: Opt) -> T5ForConditionalGeneration:
      if not check_hf_version(HF_VERSION):
          raise RuntimeError(
              "Must use transformers package >= 4.3 to use t5"
          )
      torch_dtype = (
          torch.float16
          if (opt["fp16"] and not delay_halving(opt))
          else torch.float32
      )
      try:
          return T5ForConditionalGeneration.from_pretrained(
              opt["t5_model_arch"],
              dropout_rate=opt["t5_dropout"],
              torch_dtype=torch_dtype,
          )
      except TypeError:
          # it's not clear when HF added the `torch_dtype` option, but it is not
          # available in 4.3.3, which is the earliest we support.
          return T5ForConditionalGeneration.from_pretrained(
              opt["t5_model_arch"], dropout_rate=opt["t5_dropout"]
          )
  
imports: import torch
  from typing import Optional, Dict, Any, Tuple
  from transformers import T5ForConditionalGeneration
  from parlai.agents.hugging_face.hugging_face import HF_VERSION
  from parlai.agents.hugging_face.dict import T5DictionaryAgent
  from parlai.core.opt import Opt
  from parlai.core.params import ParlaiParser
  from parlai.core.torch_agent import Batch, TorchAgent
  from parlai.core.torch_generator_agent import TorchGeneratorAgent, TorchGeneratorModel
  from parlai.utils.fsdp import is_fsdp, delay_halving
label_desc: Use image node_id 2 for calling a global function with example usage: build_t5(opt) and returns: T5ForConditionalGeneration, T5ForConditionalGeneration