Has anyone tried getting llm to write

from langchain.chains.openai_functions import (
    convert_to_openai_function,
    get_openai_output_parser,
)
from langchain.pydantic_v1 import BaseModel, Field

# Example usage
json_schema = '''
{
    "title": "TypeOfLaw",
    "description": "Identifying information about the types of law that attorneys practice.",
    "properties": {
        "criminal_law": {"type": "bool"},
        "civil_rights_law": {"type": "bool"},
        "family_law": {"type": "bool"},
        "real_estate_law": {"type": "bool"},
        "corporate_law": {"type": "bool"},
        "insurance_law": {"type": "bool"},
        "labor_and_employment_law": {"type": "bool"},
        "environmental_law": {"type": "bool"},
        "personal_injury_law": {"type": "bool"},
        "immigration_law": {"type": "bool"},
        "intellectual_property_law": {"type": "bool"},
        "tax_law": {"type": "bool"},
    "required": []
}
'''

# Parse the JSON schema
schema = json.loads(json_schema)
properties = schema.get('properties', {})

# Function to generate Python code for the Pydantic model from the JSON schema
def generate_pydantic_model_code(schema_properties, title, description):
    # Start of the class definition
    model_code = f"class {title}(BaseModel):\n"
    model_code += f"    \"\"\"{description}\"\"\"\n"

    # Add each field to the class definition
    for field in schema_properties:
        model_code += f"    {field}: Optional[bool] = None\n"

    return model_code

# Generate the Python code for the Pydantic model
model_code = generate_pydantic_model_code(properties, schema['title'], schema['description'])

# Execute the generated code to create the Pydantic model
exec(model_code, globals())

# Retrieve the dynamically created model class
DynamicallyCreatedModel = globals()[schema['title']]

openai_functions = [convert_to_openai_function(DynamicallyCreatedModel)]