class_docstr: A worker class that executes (a partition of) the model on a GPU.
  
  Each worker is associated with a single GPU. The worker is responsible for
  maintaining the KV cache and executing the model on the GPU. In case of
  distributed inference, each worker is assigned a partition of the model.
docstr: This function cleans the finished sequences and their KVCache in self.kv_cache
docstr_tok: ["This","function","cleans","the","finished","sequences","and","their","KVCache","in","self.kv_cache"]
code: def clean_finished_seqs(self, finished_seqs: List[int]):
      """
      This function cleans the finished sequences and their KVCache in self.kv_cache
      """
      pass
  
imports: import os
  from typing import Dict, List, Tuple, Optional
  import torch
  import torch.distributed
  import warnings
  import numpy
  import random
  from bigdl.llm.vllm.config import ModelConfig, SchedulerConfig
  from bigdl.llm.vllm.model_executor.model_loader import get_model
  from bigdl.llm.vllm.model_executor.input_metadata import InputMetadata
  from bigdl.llm.vllm.sampling_params import SamplingParams
  from bigdl.llm.vllm.sequence import SequenceData, SamplerOutput, SequenceGroupMetadata
  from bigdl.llm.utils.common import invalidInputError
  from bigdl.llm.vllm.model_executor.utils import set_random_seed
label_desc: Use image node_id 2 for calling the Worker obj's underlying member method code with example usage: obj.clean_finished_seqs(finished_seqs) without return types