code: def _pad_tensors_to_max_len(self, tensor, max_length):
      if self.tokenizer is not None and hasattr(
          self.tokenizer, "pad_token_id"
      ):
          # If PAD token is not defined at least EOS token has to be defined
          pad_token_id = (
              self.tokenizer.pad_token_id
              if self.tokenizer.pad_token_id is not None
              else self.tokenizer.eos_token_id
          )
      else:
          if self.model.config.pad_token_id is not None:
              pad_token_id = self.model.config.pad_token_id
          else:
              raise ValueError(
                  "Pad_token_id must be set in the configuration of the model, in order to pad tensors"
              )
  
      padded_tensor = pad_token_id * torch.ones(
          (tensor.shape[0], max_length),
          dtype=tensor.dtype,
          device=tensor.device,
      )
      padded_tensor[:, : tensor.shape[-1]] = tensor
      return padded_tensor
  
file_path: optimum/optimum/onnxruntime/trainer_seq2seq.py
imports: from typing import Any, Dict, List, Optional, Tuple, Union
  import torch
  from torch import nn
  from torch.utils.data import Dataset
  from transformers.deepspeed import is_deepspeed_zero3_enabled
  from transformers.trainer_utils import PredictionOutput
  from transformers.utils import is_accelerate_available, logging
  from .trainer import ORTTrainer
label_desc: Use image node_id 4 for calling the ORTSeq2SeqTrainer obj's underlying member method code with example usage: obj._pad_tensors_to_max_len(tensor, max_length) and returns: padded_tensor