class_docstr: Client for the Microsoft's Megatron-Turing NLG models (https://arxiv.org/abs/2201.11990).
  
  According to the internal documentation: https://github.com/microsoft/turing-academic-TNLG,
  "the model will generate roughly 3 tokens per second. The response will be returned once
  all tokens have been generated."
code: def __init__(
      self,
      lock_file_path: str,
      cache_config: CacheConfig,
      api_key: Optional[str] = None,
      org_id: Optional[str] = None,
  ):
      # Adapted from their documentation: https://github.com/microsoft/turing-academic-TNLG
      class EngineAPIResource(engine_api_resource.EngineAPIResource):
          @classmethod
          def class_url(
              cls,
              engine: Optional[str] = None,
              api_type: Optional[str] = None,
              api_version: Optional[str] = None,
          ) -> str:
              return f"/{engine}/inference"
  
      self.org_id: Optional[str] = org_id
      self.api_key: Optional[str] = api_key
      self.api_base: str = "https://turingnlg-turingnlg-mstap-v2.turingase.p.azurewebsites.net"
      self.completion_attributes = (
          EngineAPIResource,
      ) + ORIGINAL_COMPLETION_ATTRIBUTES[1:]
  
      self.cache = Cache(cache_config)
  
      # The Microsoft Turing server only allows a single request at a time, so acquire a
      # process-safe lock before making a request.
      # https://github.com/microsoft/turing-academic-TNLG#rate-limitations
      #
      # Since the model will generate roughly three tokens per second and the max context window
      # is 2048 tokens, we expect the maximum time for a request to be fulfilled to be 700 seconds.
      self._lock = FileLock(lock_file_path, timeout=700)
  
file_path: H2O/h2o_hf/helm/src/helm/proxy/clients/microsoft_client.py
imports: from typing import List, Optional, Dict
  from filelock import FileLock
  from openai.api_resources.abstract import engine_api_resource
  import openai
  from helm.common.cache import Cache, CacheConfig
  from helm.common.request import EMBEDDING_UNAVAILABLE_REQUEST_RESULT, Request, RequestResult, Sequence, Token
  from helm.common.tokenization_request import TokenizationRequest, TokenizationRequestResult, DecodeRequest, DecodeRequestResult
  from .client import Client, wrap_request_time, truncate_sequence
  from .openai_client import ORIGINAL_COMPLETION_ATTRIBUTES
label_desc: Use image node_id 2 to create a new MicrosoftClient object from inherited base classes: Client with example: obj = MicrosoftClient(lock_file_path, cache_config, api_key, org_id)