# Buggy code
def _save_model_with_loader_module_and_data_path(
    path,
    loader_module,
    data_path=None,
    code_paths=None,
    conda_env=None,
    mlflow_model=Model(),
):
    """
    Export model as a generic Python function model.
    :param path: The path to which to save the Python model.
    :param loader_module: The name of the Python module that is used to load the model
                          from ``data_path``. This module must define a method with the prototype
                          ``_load_pyfunc(data_path)``.
    :param data_path: Path to a file or directory containing model data.
    :param code_paths: A list of local filesystem paths to Python file dependencies (or directories
                      containing file dependencies). These files are *prepended* to the system
                      path before the model is loaded.
    :param conda_env: Either a dictionary representation of a Conda environment or the path to a
                      Conda environment yaml file. If provided, this decsribes the environment
                      this model should be run in.
    :return: Model configuration containing model info.
    """
    if os.path.exists(path):
        raise MlflowException(
            message="Path '{}' already exists".format(path),
            error_code=RESOURCE_ALREADY_EXISTS,
        )
    os.makedirs(path)

    code = None
    data = None
    env = None

    if data_path is not None:
        model_file = _copy_file_or_tree(src=data_path, dst=path, dst_dir="data")
        data = model_file

    if code_paths is not None:
        for code_path in code_paths:
            _copy_file_or_tree(src=code_path, dst=path, dst_dir="code")
        code = "code"

    if conda_env is not None:
        shutil.copy(src=conda_env, dst=os.path.join(path, "mlflow_env.yml"))
        env = "mlflow_env.yml"

    mlflow.pyfunc.add_to_model(
        mlflow_model, loader_module=loader_module, code=code, data=data, env=env
    )
    mlflow_model.save(os.path.join(path, "MLmodel"))
    return mlflow_model


# Traceback (TypeError)
import mlflow.pyfunc
mlflow.pyfunc.log_model('x', 'x', conda_env={})
Traceback (most recent call last):
...
TypeError: stat: path should be string, bytes, os.PathLike or integer, not dict

# Fixed code
def _save_model_with_loader_module_and_data_path(
    path,
    loader_module,
    data_path=None,
    code_paths=None,
    conda_env=None,
    mlflow_model=Model(),
):
    """
    Export model as a generic Python function model.
    :param path: The path to which to save the Python model.
    :param loader_module: The name of the Python module that is used to load the model
                          from ``data_path``. This module must define a method with the prototype
                          ``_load_pyfunc(data_path)``.
    :param data_path: Path to a file or directory containing model data.
    :param code_paths: A list of local filesystem paths to Python file dependencies (or directories
                      containing file dependencies). These files are *prepended* to the system
                      path before the model is loaded.
    :param conda_env: Either a dictionary representation of a Conda environment or the path to a
                      Conda environment yaml file. If provided, this decsribes the environment
                      this model should be run in.
    :return: Model configuration containing model info.
    """
    if os.path.exists(path):
        raise MlflowException(
            message="Path '{}' already exists".format(path),
            error_code=RESOURCE_ALREADY_EXISTS,
        )
    os.makedirs(path)

    code = None
    data = None

    if data_path is not None:
        model_file = _copy_file_or_tree(src=data_path, dst=path, dst_dir="data")
        data = model_file

    if code_paths is not None:
        for code_path in code_paths:
            _copy_file_or_tree(src=code_path, dst=path, dst_dir="code")
        code = "code"

    conda_env_subpath = "mlflow_env.yml"
    if conda_env is None:
        conda_env = get_default_conda_env()
    elif not isinstance(conda_env, dict):
        with open(conda_env, "r") as f:
            conda_env = yaml.safe_load(f)
    with open(os.path.join(path, conda_env_subpath), "w") as f:
        yaml.safe_dump(conda_env, stream=f, default_flow_style=False)

    mlflow.pyfunc.add_to_model(
        mlflow_model,
        loader_module=loader_module,
        code=code,
        data=data,
        env=conda_env_subpath,
    )
    mlflow_model.save(os.path.join(path, "MLmodel"))
    return mlflow_model