class_docstr: The model output associated with a sequence.
  
  Args:
      parent_seq_id: The ID of the parent sequence (for forking in beam
          search).
      output_token: The output token ID.
      logprobs: The logprobs of the output token.
          (Token id -> logP(x_i+1 | x_0, ..., x_i))
class_docstr_tok: ["The","model","output","associated","with","a","sequence",".","Args",":","parent_seq_id",":","The","ID","of","the","parent","sequence","(","for","forking","in","beam","search",")",".","output_token",":","The","output","token","ID",".","logprobs",":","The","logprobs","of","the","output","token",".","(","Token","id","-",">","logP","(","x_i+1","|","x_0",",","...",",","x_i",")",")"]
code: def __init__(
      self,
      parent_seq_id: int,
      output_token: int,
      latency: float,
      logprobs: Dict[int, float],
  ) -> None:
      self.parent_seq_id = parent_seq_id
      self.output_token = output_token
      self.logprobs = logprobs
      self.latency = latency
  
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 SequenceOutputs object  with example: obj = SequenceOutputs(parent_seq_id, output_token, latency, logprobs)