code: def _left_padding(self, batch_inputs):
      max_len = max(len(inputs["input_ids"]) for inputs in batch_inputs)
      outputs = {"input_ids": [], "attention_mask": []}
      for inputs in batch_inputs:
          input_ids, attention_mask = (
              inputs["input_ids"],
              inputs["attention_mask"],
          )
          padding_len = max_len - len(input_ids)
          input_ids = [self.pad_token_id] * padding_len + input_ids
          attention_mask = [0] * padding_len + attention_mask
          outputs["input_ids"].append(input_ids)
          outputs["attention_mask"].append(attention_mask)
      for k in outputs:
          outputs[k] = torch.tensor(outputs[k])
      return outputs, max_len
  
file_path: colossalai/examples/tutorial/opt/inference/batch.py
imports: from typing import Any, Deque, Hashable, List, Tuple
  import torch
  from energonai import BatchManager, SubmitEntry, TaskEntry
label_desc: Use image node_id 2 for calling the BatchManagerForGeneration obj's underlying member method code with example usage: obj._left_padding(batch_inputs) and returns: outputs,  max_len