code: def _get_and_verify_max_len(
      hf_config: PretrainedConfig,
      max_model_len: Optional[int],
  ) -> int:
      """Get and verify the model's maximum length."""
      derived_max_model_len = float("inf")
      possible_keys = [
          # OPT
          "max_position_embeddings",
          # GPT-2
          "n_positions",
          # MPT
          "max_seq_len",
          # Others
          "max_sequence_length",
          "max_seq_length",
          "seq_len",
      ]
      for key in possible_keys:
          max_len_key = getattr(hf_config, key, None)
          if max_len_key is not None:
              derived_max_model_len = min(
                  derived_max_model_len, max_len_key
              )
      if derived_max_model_len == float("inf"):
          if max_model_len is not None:
              # If max_model_len is specified, we use it.
              return max_model_len
  
          default_max_len = 2048
          logger.warning(
              "The model's config.json does not contain any of the following "
              "keys to determine the original maximum length of the model: "
              f"{possible_keys}. Assuming the model's maximum length is "
              f"{default_max_len}."
          )
          derived_max_model_len = default_max_len
  
      rope_scaling = getattr(hf_config, "rope_scaling", None)
      if rope_scaling is not None:
          invalidInputError(
              "factor" in rope_scaling,
              "invalid hf_config value for rope_scaling",
          )
          scaling_factor = rope_scaling["factor"]
          derived_max_model_len *= scaling_factor
  
      if max_model_len is None:
          max_model_len = derived_max_model_len
      elif max_model_len > derived_max_model_len:
          invalidInputError(
              False,
              f"User-specified max_model_len ({max_model_len}) is greater than "
              f"the derived max_model_len ({max_len_key}={derived_max_model_len}"
              " in model's config.json). This may lead to incorrect model "
              "outputs or CUDA errors. Make sure the value is correct and "
              "within the model context size.",
          )
      return int(max_model_len)
  
imports: from typing import Optional
  import torch
  from transformers import AutoConfig, PretrainedConfig
  from bigdl.llm.vllm.logger import init_logger
  from bigdl.llm.utils.common import invalidInputError
label_desc: Use image node_id 2 for calling a global function with example usage: _get_and_verify_max_len(hf_config, max_model_len) and returns: int, max_model_len