class_docstr: Data associated with a sequence.
  
  
  Args:
      prompt_token_ids: The token IDs of the prompt.
  
  Attributes:
      prompt_token_ids: The token IDs of the prompt.
      output_token_ids: The token IDs of the output.
      cumulative_logprob: The cumulative log probability of the output.
class_docstr_tok: ["Data","associated","with","a","sequence",".","Args",":","prompt_token_ids",":","The","token","IDs","of","the","prompt",".","Attributes",":","prompt_token_ids",":","The","token","IDs","of","the","prompt",".","output_token_ids",":","The","token","IDs","of","the","output",".","cumulative_logprob",":","The","cumulative","log","probability","of","the","output","."]
code: def __init__(
      self,
      prompt_token_ids: List[int],
  ) -> None:
      self.prompt_token_ids = prompt_token_ids
      self.output_token_ids: List[int] = []
      self.cumulative_logprob = 0.0
      self.created_timestamp = time.perf_counter()
      self.updated_timestamp = self.created_timestamp
      self.last_token_latency = 0.0
  
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 SequenceData object  with example: obj = SequenceData(prompt_token_ids)