class_docstr: Wrap T5 in ParlAI.
class_docstr_tok: ["Wrap","T5","in","ParlAI","."]
docstr: Return initial input to the decoder.
  
  :param bsz:
      batchsize
  :param inputs:
      inputs to decode
  
  :return initial_input:
      initial input for the decoder.
docstr_tok: ["Return","initial","input","to","the","decoder",".",":","param","bsz",":","batchsize",":","param","inputs",":","inputs","to","decode",":","return","initial_input",":","initial","input","for","the","decoder","."]
code: def _get_initial_forced_decoder_input(
      self, bsz: int, inputs: torch.LongTensor
  ):
      """
      Return initial input to the decoder.
  
      :param bsz:
          batchsize
      :param inputs:
          inputs to decode
  
      :return initial_input:
          initial input for the decoder.
      """
      inputs = torch.cat(
          [self.START.detach().expand(bsz, 1), inputs], 1
      )
      return inputs
  
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 the ParlaiT5Model obj's underlying member method code with example usage: obj._get_initial_forced_decoder_input(bsz, inputs) and returns: inputs