```python
import pytest
from faker import Faker
from collections import namedtuple
from dataclasses import dataclass
from typing import Optional, List, Union
from jinja2 import Template
import tempfile
import os
import openai
import time
import logging
import json

# The name of the default model used.
DEFAULT_MODEL = "gpt-3.5-turbo-0613"
# The default prompt used when calling the function.
DEFAULT_PROMPT = "Translate the following English text to French: '{}'"
# The initial message provided to the model
DEFAULT_SYS_MSG = "A LLM 7 AGI Hive-Mind simulator"
# The maximum number of times to retry if there is a failure
DEFAULT_MAX_RETRY = 1
# Multiplier to use for backoff time between retries
DEFAULT_BACKOFF_FACTOR = 2
# Initial wait time before retrying after a failure
DEFAULT_INITIAL_WAIT = 0.25

@dataclass
class ComplexMultiLineTemplate(TypedTemplate):
    class_name: str
    attributes: list
    methods: list

    source = """class {{ class_name }}:
    def __init__(self{% for attr in attributes %}, {{ attr.name }}: {{ attr.type }}{% endfor %}):
        {% for attr in attributes -%}
        self.{{ attr.name }} = {{ attr.name }}
        {% endfor %}
    {%- for method in methods %}
    def {{ method.name }}(self{% for param in method.params %}, {{ param.name }}: {{ param.type }}{% endfor %}):
        return "{{ faker_sentence() }}"  # Simulating logic with Faker sentence{% endfor %}"""

# A fixture that generates a complex multilined template
@pytest.fixture
def rendered_complex_multiline_template():
    faker = Faker()
    Attribute = namedtuple('Attribute', ['name', 'type'])
    Method = namedtuple('Method', ['name', 'params'])
    Param = namedtuple('Param', ['name', 'type'])

    attributes = [Attribute(name=faker.word(), type=faker.word()) for _ in range(3)]
    methods = [Method(name=faker.word(), params=[Param(name=faker.word(), type=faker.word()) for _ in range(2)]) for _ in range(3)]

    template = ComplexMultiLineTemplate(class_name=faker.word().capitalize(), attributes=attributes, methods=methods)

    return template.render()

def bool_prompt(prompt: str, sys_msg: str = "A LLM 7 AGI Hive-Mind simulator", msgs: Optional[List[dict]] = None,
                funcs: Optional[List[dict]] = None, model: str = "gpt-3.5-turbo-0613",
                max_retry: int = 1, backoff_factor: int = 2, initial_wait: float = 0.25) -> bool:
    response = chat(prompt=prompt, sys_msg=sys_msg, msgs=msgs, funcs=funcs, model=model,
                    max_retry=max_retry, backoff_factor=backoff_factor, initial_wait=initial_wait)
    return response.lower() in ["yes", "true", "y", "t"]

@dataclass(frozen=True)
class Chat:
    prompt: str = ""
    sys_msg: str = "A LLM 7 AGI Hive-Mind simulator"
    msgs: Optional[List[dict]] = field(default_factory=list)
    funcs: Optional[List[dict]] = None
    model: str = "gpt-3.5-turbo-0613"
    max_retry: int = 1
    backoff_factor: int = 2
    initial_wait: float = 0.25

    def __call__(self) -> Union[str, dict]:
        import openai
        import os
        openai.api_key = os.getenv("OPENAI_API_KEY")
        messages = [
            {"role": "system", "content": self.sys_msg},
            {"role": "user", "content": self.prompt},
        ]
        messages.extend(self.msg