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.
code: def execute_model(
      self,
      seq_group_metadata_list: List[SequenceGroupMetadata],
      blocks_to_swap_in: Dict[int, int],
      blocks_to_swap_out: Dict[int, int],
      blocks_to_copy: Dict[int, List[int]],
      finished_seqs: List[int],
  ) -> SamplerOutput:
      # Issue cache operations.
      # issued_cache_op = False
      # if blocks_to_swap_in:
      #     self.cache_engine.swap_in(blocks_to_swap_in)
      #     issued_cache_op = True
      # if blocks_to_swap_out:
      #     self.cache_engine.swap_out(blocks_to_swap_out)
      #     issued_cache_op = True
      # if blocks_to_copy:
      #     self.cache_engine.copy(blocks_to_copy)
      #     issued_cache_op = True
  
      # if issued_cache_op:
      #     cache_events = self.cache_events
      # else:
      #     cache_events = None
      if finished_seqs:
          self.clean_finished_seqs(finished_seqs)
  
      # if self.model_config.device == 'xpu':
      #     import intel_extension_for_pytorch as ipex
      #     torch.xpu.empty_cache()
  
      cache_events = None
      # If there is no input, we don't need to execute the model.
      if not seq_group_metadata_list:
          if cache_events is not None:
              for event in cache_events:
                  event.wait()
          return {}
  
      # pdb.set_trace()
      # TODO(gc): use environment/global virable to check
      if True:
          (
              input_tokens,
              input_positions,
              input_metadata,
          ) = self._prepare_inputs(seq_group_metadata_list)
          output = self.model(
              seq_group_meta_data_lists=seq_group_metadata_list,
              kv_cache=self.kv_cache,
              input_metadata=input_metadata,
          )
          return output
      else:
          # Prepare input tensors.
          (
              input_tokens,
              input_positions,
              input_metadata,
          ) = self._prepare_inputs(seq_group_metadata_list)
  
          # Execute the model.
          output = self.model(
              input_ids=input_tokens,
              positions=input_positions,
              kv_caches=None,
              input_metadata=input_metadata,
              cache_events=cache_events,
          )
      return output
  
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 5 for calling the Worker obj's underlying member method code with example usage: obj.execute_model(seq_group_metadata_list, blocks_to_swap_in, blocks_to_swap_out, blocks_to_copy, finished_seqs) and returns: output, dict, output