class_docstr: Scheduler configuration.
  
  Args:
      max_num_batched_tokens: Maximum number of tokens to be processed in
          a single iteration.
      max_num_seqs: Maximum number of sequences to be processed in a single
          iteration.
      max_model_len: Maximum length of a sequence (including prompt
          and generated text).
class_docstr_tok: ["Scheduler","configuration",".","Args",":","max_num_batched_tokens",":","Maximum","number","of","tokens","to","be","processed","in","a","single","iteration",".","max_num_seqs",":","Maximum","number","of","sequences","to","be","processed","in","a","single","iteration",".","max_model_len",":","Maximum","length","of","a","sequence","(","including","prompt","and","generated","text",")","."]
code: def _verify_args(self) -> None:
      if self.max_num_batched_tokens < self.max_model_len:
          invalidInputError(
              f"max_num_batched_tokens ({self.max_num_batched_tokens}) is "
              f"smaller than max_model_len ({self.max_model_len}). "
              "This effectively limits the maximum sequence length to "
              "max_num_batched_tokens and makes vLLM reject longer "
              "sequences. Please increase max_num_batched_tokens or "
              "decrease max_model_len."
          )
      if self.max_num_batched_tokens < self.max_num_seqs:
          invalidInputError(
              f"max_num_batched_tokens ({self.max_num_batched_tokens}) must "
              "be greater than or equal to max_num_seqs "
              f"({self.max_num_seqs})."
          )
  
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 the SchedulerConfig obj's underlying member method code with example usage: obj._verify_args() without return types