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: Creates an LLM engine from the engine arguments.
docstr_tok: ["Creates","an","LLM","engine","from","the","engine","arguments","."]
code: def from_engine_args(cls, engine_args: EngineArgs) -> "LLMEngine":
      """Creates an LLM engine from the engine arguments."""
      # bigdl-llm change start
      # summary: remove parallel_config and related settings.
      # Create the engine configs.
      engine_configs = engine_args.create_engine_configs()
      # parallel_config = engine_configs[2]
      # Initialize cluster locally.
      # port = get_open_port()
      # We need to setup the distributed init method to make sure
      # the distributed megatron code (e.g., get world size) works correctly.
      # distributed_init_method = f"tcp://localhost:{port}"
      # Create the LLM engine.
      engine = cls(
          *engine_configs,
          # distributed_init_method,
          # None,
          log_stats=not engine_args.disable_log_stats,
      )
      return engine
  
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 4 for calling the LLMEngine obj's underlying member method code with example usage: obj.from_engine_args(cls, engine_args) and returns: engine