code: def get_first_decoder_step_input(
      beam_size: int = 5, eos_token_id: int = 0, src_length: int = 1
  ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
      prev_tokens = torch.full(
          [beam_size], eos_token_id, dtype=torch.long
      )
      prev_scores = torch.full([beam_size], 1, dtype=torch.float)
      prev_hypos = torch.full([beam_size], 0, dtype=torch.long)
      attention_weights = torch.full(
          [beam_size, src_length], 1, dtype=torch.float
      )
      return prev_tokens, prev_scores, prev_hypos, attention_weights
  
file_path: pytext/pytext/torchscript/seq2seq/beam_search.py
imports: from typing import List, Optional, Tuple
  import torch
  import torch.jit
  import torch.jit.quantized
  from pytext.common.constants import TORCH_VERSION
  from torch import nn
  from .decoder import DecoderBatchedStepEnsemble
  from .encoder import EncoderEnsemble
label_desc: Use image node_id 1 for calling a global function with example usage: get_first_decoder_step_input(beam_size, eos_token_id, src_length) and returns: prev_tokens,  prev_scores,  prev_hypos,  attention_weights