class_docstr: An LLM engine that receives requests and generates texts.
  
  This is the main class for the vLLM engine. It receives requests
  from clients and generates texts from the LLM. It includes a tokenizer, a
  language model (possibly distributed across multiple GPUs), and GPU memory
  space allocated for intermediate states (aka KV cache). This class utilizes
  iteration-level scheduling and efficient memory management to maximize the
  serving throughput.
  
  The `LLM` class wraps this class for offline batched inference and the
  `AsyncLLMEngine` class wraps this class for online serving.
  
  NOTE: The config arguments are derived from the `EngineArgs` class. For the
  comprehensive list of arguments, see `EngineArgs`.
  
  Args:
      model_config: The configuration related to the LLM model.
      cache_config: The configuration related to the KV cache memory
          management.
      parallel_config: The configuration related to distributed execution.
      scheduler_config: The configuration related to the request scheduler.
      distributed_init_method: The initialization method for distributed
          execution. See `torch.distributed.init_process_group` for details.
      placement_group: Ray placement group for distributed execution.
          Required for distributed execution.
      log_stats: Whether to log statistics.
docstr: Decodes the new token for a sequence.
docstr_tok: ["Decodes","the","new","token","for","a","sequence","."]
code: def _decode_sequence(
      self, seq: Sequence, sampling_params: SamplingParams
  ) -> None:
      """Decodes the new token for a sequence."""
      (
          new_tokens,
          new_output_text,
          prefix_offset,
          read_offset,
      ) = detokenize_incrementally(
          self.tokenizer,
          all_input_ids=seq.get_token_ids(),
          prev_tokens=seq.tokens,
          prefix_offset=seq.prefix_offset,
          read_offset=seq.read_offset,
          skip_special_tokens=sampling_params.skip_special_tokens,
      )
      if seq.tokens is None:
          seq.tokens = new_tokens
      else:
          seq.tokens.extend(new_tokens)
      seq.prefix_offset = prefix_offset
      seq.read_offset = read_offset
      seq.output_text += new_output_text
  
imports: import time
  from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Union, Dict
  from bigdl.llm.vllm.config import ModelConfig, SchedulerConfig
  from bigdl.llm.vllm.core.scheduler import SchedulerOutputs, FixedWindowScheduler
  from bigdl.llm.vllm.engine.arg_utils import EngineArgs
  from bigdl.llm.vllm.logger import init_logger
  from bigdl.llm.vllm.outputs import RequestOutput
  from bigdl.llm.vllm.sampling_params import SamplingParams
  from bigdl.llm.vllm.sequence import SamplerOutput, Sequence, SequenceGroup, SequenceGroupMetadata, SequenceStatus, SequenceOutputs
  from bigdl.llm.vllm.transformers_utils.tokenizer import get_tokenizer, detokenize_incrementally
  from bigdl.llm.vllm.utils import Counter
  from bigdl.llm.utils.common import invalidInputError
label_desc: Use image node_id 16 for calling the LLMEngine obj's underlying member method code with example usage: obj._decode_sequence(seq, sampling_params) without return types