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 __init__(
      self,
      max_num_batched_tokens: Optional[int],
      max_num_seqs: int,
      max_model_len: int,
  ) -> None:
      if max_num_batched_tokens is not None:
          self.max_num_batched_tokens = max_num_batched_tokens
      else:
          # If max_model_len is too short, use 2048 as the default value for
          # higher throughput.
          self.max_num_batched_tokens = max(max_model_len, 2048)
      self.max_num_seqs = max_num_seqs
      self.max_model_len = max_model_len
      self._verify_args()
  
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 1 to create a new SchedulerConfig object  with example: obj = SchedulerConfig(max_num_batched_tokens, max_num_seqs, max_model_len)