code: def __init__(
      self,
      decoding_type_schema=None,
      task="event",
      decoding_format="tree",
      source_prefix=None,
      *args,
      **kwargs,
  ):
      super().__init__(*args, **kwargs)
  
      self.decoding_format = decoding_format
      self.decoding_type_schema = decoding_type_schema
  
      # Label smoothing by sum token loss, different from different Label smootheing
      if self.args.label_smoothing_factor != 0:
          self.label_smoother = LabelSmoother(
              epsilon=self.args.label_smoothing_factor
          )
          print("Using %s" % self.label_smoother)
      else:
          self.label_smoother = None
  
      if self.args.constraint_decoding:
          self.constraint_decoder = get_constraint_decoder(
              tokenizer=self.tokenizer,
              type_schema=self.decoding_type_schema,
              decoding_schema=self.decoding_format,
              source_prefix=source_prefix,
              task_name=task,
          )
      else:
          self.constraint_decoder = None
  
      self.oom_batch = 0
  
imports: import torch
  import torch.nn
  from dataclasses import dataclass, field
  from typing import Union, List, Dict, Tuple, Any, Optional
  from torch.cuda.amp import autocast
  from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments
  from transformers.trainer_pt_utils import LabelSmoother
  from transformers.trainer import *
  from ..seq2seq.constraint_decoder import get_constraint_decoder
label_desc: Use image node_id 1 to create a new ConstraintSeq2SeqTrainer object from inherited base classes: Seq2SeqTrainer with example: obj = ConstraintSeq2SeqTrainer(decoding_type_schema, task, decoding_format, source_prefix)