code: def __init__(self, tgt_dicts):
      super().__init__()
      tgt_dict = list(tgt_dicts.values())[0]
      self.pad = tgt_dict.pad()
      self.unk = tgt_dict.unk()
      self.eos = tgt_dict.eos()
      for tgt_dict in tgt_dicts.values():
          assert self.pad == tgt_dict.pad()
          assert self.unk == tgt_dict.unk()
          assert self.eos == tgt_dict.eos()
      self.vocab_sizes = {
          channel: len(tgt_dicts[channel]) for channel in tgt_dicts
      }
      self.src_lengths = torch.tensor(-1)
      self.supports_constraints = False
      self.stop_on_max_len = False
  
imports: from typing import Dict, Optional
  import torch
  import torch.nn
  from torch import Tensor
label_desc: Use image node_id 1 to create a new MultichannelSearch object from inherited base classes: nn with example: obj = MultichannelSearch(tgt_dicts)