class_docstr: A group of sequences that are generated from the same prompt.
  
  Args:
      request_id: The ID of the request.
      seqs: The list of sequences.
      sampling_params: The sampling parameters used to generate the outputs.
      arrival_time: The arrival time of the request.
class_docstr_tok: ["A","group","of","sequences","that","are","generated","from","the","same","prompt",".","Args",":","request_id",":","The","ID","of","the","request",".","seqs",":","The","list","of","sequences",".","sampling_params",":","The","sampling","parameters","used","to","generate","the","outputs",".","arrival_time",":","The","arrival","time","of","the","request","."]
code: def __init__(
      self,
      request_id: str,
      seqs: List[Sequence],
      sampling_params: SamplingParams,
      arrival_time: float,
  ) -> None:
      self.request_id = request_id
      self.seqs_dict = {seq.seq_id: seq for seq in seqs}
      self.sampling_params = sampling_params
      self.arrival_time = arrival_time
  
imports: import copy
  import enum
  import time
  from typing import Dict, List, Optional, Union
  from bigdl.llm.vllm.sampling_params import SamplingParams
  from bigdl.llm.utils.common import invalidInputError
label_desc: Use image node_id 1 to create a new SequenceGroup object  with example: obj = SequenceGroup(request_id, seqs, sampling_params, arrival_time)