class_docstr: (In preview) A class for generic conversable agents which can be configured as assistant or user proxy.
  
  After receiving each message, the agent will send a reply to the sender unless the msg is a termination msg.
  For example, AssistantAgent and UserProxyAgent are subclasses of this class,
  configured with different default settings.
  
  To modify auto reply, override `generate_reply` method.
  To disable/enable human response in every turn, set `human_input_mode` to "NEVER" or "ALWAYS".
  To modify the way to get human input, override `get_human_input` method.
  To modify the way to execute code blocks, single code block, or function call, override `execute_code_blocks`,
  `run_code`, and `execute_function` methods respectively.
  To customize the initial message when a conversation starts, override `generate_init_message` method.
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docstr: Generate a reply using autogen.oai.
docstr_tok: ["Generate","a","reply","using","autogen.oai","."]
code: def generate_oai_reply(
      self,
      messages: Optional[List[Dict]] = None,
      sender: Optional[Agent] = None,
      config: Optional[OpenAIWrapper] = None,
  ) -> Tuple[bool, Union[str, Dict, None]]:
      """Generate a reply using autogen.oai."""
      client = self.client if config is None else config
      if client is None:
          return False, None
      if messages is None:
          messages = self._oai_messages[sender]
  
      # unroll tool_responses
      all_messages = []
      for message in messages:
          tool_responses = message.get("tool_responses", [])
          if tool_responses:
              all_messages += tool_responses
              # tool role on the parent message means the content is just concatenation of all of the tool_responses
              if message.get("role") != "tool":
                  all_messages.append(
                      {
                          key: message[key]
                          for key in message
                          if key != "tool_responses"
                      }
                  )
          else:
              all_messages.append(message)
  
      # TODO: #1143 handle token limit exceeded error
      response = client.create(
          context=messages[-1].pop("context", None),
          messages=self._oai_system_message + all_messages,
      )
  
      extracted_response = client.extract_text_or_completion_object(
          response
      )[0]
  
      # ensure function and tool calls will be accepted when sent back to the LLM
      if not isinstance(extracted_response, str):
          extracted_response = model_dump(extracted_response)
      if isinstance(extracted_response, dict):
          if extracted_response.get("function_call"):
              extracted_response["function_call"][
                  "name"
              ] = self._normalize_name(
                  extracted_response["function_call"]["name"]
              )
          for tool_call in extracted_response.get("tool_calls") or []:
              tool_call["function"]["name"] = self._normalize_name(
                  tool_call["function"]["name"]
              )
      return True, extracted_response
  
imports: import asyncio
  import copy
  import functools
  import inspect
  import json
  import logging
  import re
  from collections import defaultdict
  from typing import Any, Awaitable, Callable, Dict, List, Literal, Optional, Tuple, Type, TypeVar, Union
  from ..None import OpenAIWrapper
  from ..code_utils import DEFAULT_MODEL, UNKNOWN, content_str, execute_code, extract_code, infer_lang
  from ..function_utils import get_function_schema, load_basemodels_if_needed, serialize_to_str
  from .agent import Agent
  from .._pydantic import model_dump
label_desc: Use image node_id 25 for calling the ConversableAgent obj's underlying member method code with example usage: obj.generate_oai_reply(messages, sender, config) and returns: True,  extracted_response, False,  None