@with_callbacks
def __call__(self, *args, **kwargs) -> Prediction:
    from dspy.dsp.utils.settings import thread_local_overrides

    caller_modules = settings.caller_modules or []
    caller_modules = list(caller_modules)
    caller_modules.append(self)

    with settings.context(caller_modules=caller_modules):
        if settings.track_usage and thread_local_overrides.get().get("usage_tracker") is None:
            with track_usage() as usage_tracker:
                output = self.forward(*args, **kwargs)
            tokens = usage_tracker.get_total_tokens()
            self._set_lm_usage(tokens, output)

            return output

        return self.forward(*args, **kwargs)

def batch(
    self,
    examples: list[Example],
    num_threads: int | None = None,
    max_errors: int | None = None,
    return_failed_examples: bool = False,
    provide_traceback: bool | None = None,
    disable_progress_bar: bool = False,
    timeout: int = 120,
    straggler_limit: int = 3,
) -> list[Example] | tuple[list[Example], list[Example], list[Exception]]:
    """
    Processes a list of dspy.Example instances in parallel using the Parallel module.

    Args:
        examples: List of dspy.Example instances to process.
        num_threads: Number of threads to use for parallel processing.
        max_errors: Maximum number of errors allowed before stopping execution.
            If ``None``, inherits from ``dspy.settings.max_errors``.
        return_failed_examples: Whether to return failed examples and exceptions.
        provide_traceback: Whether to include traceback information in error logs.
        disable_progress_bar: Whether to display the progress bar.
        timeout: Seconds before a straggler task is resubmitted. Set to 0 to disable.
        straggler_limit: Only check for stragglers when this many or fewer tasks remain.

    Returns:
        List of results, and optionally failed examples and exceptions.
    """
    # Create a list of execution pairs (self, example)
    exec_pairs = [(self, example.inputs()) for example in examples]

    # Create an instance of Parallel
    parallel_executor = Parallel(
        num_threads=num_threads,
        max_errors=max_errors,
        return_failed_examples=return_failed_examples,
        provide_traceback=provide_traceback,
        disable_progress_bar=disable_progress_bar,
        timeout=timeout,
        straggler_limit=straggler_limit,
    )

    # Execute the forward method of Parallel
    if return_failed_examples:
        results, failed_examples, exceptions = parallel_executor.forward(exec_pairs)
        return results, failed_examples, exceptions
    else:
        results = parallel_executor.forward(exec_pairs)
        return results

def deepcopy(self):
    """Deep copy the module.

    This is a tweak to the default python deepcopy that only deep copies `self.parameters()`, and for other
    attributes, we just do the shallow copy.
    """
    try:
        # If the instance itself is copyable, we can just deep copy it.
        # Otherwise we will have to create a new instance and copy over the attributes one by one.
        return copy.deepcopy(self)
    except Exception:
        pass

    # Create an empty instance.
    new_instance = self.__class__.__new__(self.__class__)
    # Set attribuetes of the copied instance.
    for attr, value in self.__dict__.items():
        if isinstance(value, BaseModule):
            setattr(new_instance, attr, value.deepcopy())
        else:
            try:
                # Try to deep copy the attribute
                setattr(new_instance, attr, copy.deepcopy(value))
            except Exception:
                logging.warning(
                    f"Failed to deep copy attribute '{attr}' of {self.__class__.__name__}, "
                    "falling back to shallow copy or reference copy."
                )
                try:
                    # Fallback to shallow copy if deep copy fails
                    setattr(new_instance, attr, copy.copy(value))
                except Exception:
                    # If even the shallow copy fails, we just copy over the reference.
                    setattr(new_instance, attr, value)

    return new_instance

def dump_state(self, json_mode=True):
    return {name: param.dump_state(json_mode=json_mode) for name, param in self.named_parameters()}

def get_lm(self):
    """Get the language model used by this module's predictors.

    Returns the language model if every module leaf uses the same LM. Raises an error if multiple
    different LMs are in use.

    Returns:
        The language model instance used by this module's predictors.

    Raises:
        ValueError: If multiple different language models are being
            used by the predictors in this module.
    """
    all_used_lms = [param.lm for _, param in self.named_parameters() if isinstance(param, Module)]

    if len(set(all_used_lms)) == 1:
        return all_used_lms[0]

    raise ValueError("Multiple LMs are being used in the module. There's no unique LM to return.")

def inspect_history(self, n: int = 1, file: "TextIO | None" = None) -> None:
    """Display the LM call history for this module.

    Prints a formatted view of the most recent language model calls
    made by this module, useful for debugging and understanding
    the module's behavior.

    Args:
        n: The number of recent history entries to display.
            Defaults to 1.
        file: An optional file-like object to write output to. When
            provided, ANSI color codes are automatically disabled.
            Defaults to `None` (prints to stdout).
    """
    pretty_print_history(self.history, n, file=file)

def load(self, path, allow_pickle=False, allow_unsafe_lm_state=False):
    """Load the saved module. You may also want to check out dspy.load, if you want to
    load an entire program, not just the state for an existing program.

    Args:
        path (str): Path to the saved state file, which should be a .json or a .pkl file
        allow_pickle (bool): If True, allow loading .pkl files, which can run arbitrary code.
            This is dangerous and should only be used if you are sure about the source of the file and in a trusted environment.
        allow_unsafe_lm_state (bool): If True, preserves unsafe LM endpoint keys (e.g.,
            `api_base`, `base_url`, and `model_list`) from loaded state and allows importing custom LM classes.
            Enable only for trusted files.
    """
    path = Path(path)

    if path.suffix == ".json":
        with open(path, "rb") as f:
            state = orjson.loads(f.read())
    elif path.suffix == ".pkl":
        if not allow_pickle:
            raise ValueError("Loading .pkl files can run arbitrary code, which may be dangerous. Prefer "
                             "saving with .json files if possible. Set `allow_pickle=True` "
                             "if you are sure about the source of the file and in a trusted environment.")
        with open(path, "rb") as f:
            state = cloudpickle.load(f)
    else:
        raise ValueError(f"`path` must end with `.json` or `.pkl`, but received: {path}")

    dependency_versions = get_dependency_versions()
    saved_dependency_versions = state["metadata"]["dependency_versions"]
    for key, saved_version in saved_dependency_versions.items():
        if dependency_versions[key] != saved_version:
            logger.warning(
                f"There is a mismatch of {key} version between saved model and current environment. "
                f"You saved with `{key}=={saved_version}`, but now you have "
                f"`{key}=={dependency_versions[key]}`. This might cause errors or performance downgrade "
                "on the loaded model, please consider loading the model in the same environment as the "
                "saving environment."
            )
    self.load_state(state, allow_unsafe_lm_state=allow_unsafe_lm_state)

def load_state(self, state, *, allow_unsafe_lm_state=False):
    from dspy import Module

    def _apply(module):
        for name, param in module.named_parameters():
            if isinstance(param, Module):
                param.load_state(state[name], allow_unsafe_lm_state=allow_unsafe_lm_state)
            else:
                param.load_state(state[name])

    _apply(self.deepcopy())  # trial run raises before self is touched
    _apply(self)

def map_named_predictors(self, func):
    """Apply a function to all named predictors in this module.

    This method iterates through all Predict instances in the module
    and applies the given function to each, replacing the original
    predictor with the function's return value.

    Args:
        func: A callable that takes a Predict instance and returns
            a new Predict instance (or compatible object).

    Returns:
        Module: Returns self for method chaining.

    Examples:
        >>> import dspy
        >>> class MyProgram(dspy.Module):
        ...     def __init__(self):
        ...         super().__init__()
        ...         self.qa = dspy.Predict("question -> answer")
        ...
        >>> program = MyProgram()
        >>> program.map_named_predictors(lambda p: p)
    """
    for name, predictor in self.named_predictors():
        set_attribute_by_name(self, name, func(predictor))
    return self

def named_parameters(self):
    """
    Unlike PyTorch, handles (non-recursive) lists of parameters too.
    """

    import dspy
    from dspy.predict.parameter import Parameter

    visited = set()
    named_parameters = []

    def add_parameter(param_name, param_value):
        if isinstance(param_value, Parameter):
            if id(param_value) not in visited:
                visited.add(id(param_value))
                named_parameters.append((param_name, param_value))

        elif isinstance(param_value, dspy.Module):
            # When a sub-module is pre-compiled, keep it frozen.
            if not getattr(param_value, "_compiled", False):
                for sub_name, param in param_value.named_parameters():
                    add_parameter(f"{param_name}.{sub_name}", param)

    if isinstance(self, Parameter):
        add_parameter("self", self)

    for name, value in self.__dict__.items():
        if isinstance(value, Parameter):
            add_parameter(name, value)

        elif isinstance(value, dspy.Module):
            # When a sub-module is pre-compiled, keep it frozen.
            if not getattr(value, "_compiled", False):
                for sub_name, param in value.named_parameters():
                    add_parameter(f"{name}.{sub_name}", param)

        elif isinstance(value, (list, tuple)):
            for idx, item in enumerate(value):
                add_parameter(f"{name}[{idx}]", item)

        elif isinstance(value, dict):
            for key, item in value.items():
                add_parameter(f"{name}['{key}']", item)

    return named_parameters

def named_predictors(self):
    """Return all named Predict modules in this module.

    Iterates through all parameters and returns those that are instances
    of ``dspy.Predict``, along with their names.

    Returns:
        list[tuple[str, Predict]]: A list of (name, predictor) tuples
            where name is the attribute path and predictor is the
            Predict instance.

    Examples:
        >>> import dspy
        >>> class MyProgram(dspy.Module):
        ...     def __init__(self):
        ...         super().__init__()
        ...         self.qa = dspy.Predict("question -> answer")
        ...         self.summarize = dspy.Predict("text -> summary")
        ...
        >>> program = MyProgram()
        >>> for name, p in program.named_predictors():
        ...     print(name)
        qa
        summarize
    """
    from dspy.predict.predict import Predict

    return [(name, param) for name, param in self.named_parameters() if isinstance(param, Predict)]

def named_sub_modules(self, type_=None, skip_compiled=False) -> Generator[tuple[str, "BaseModule"], None, None]:
    """Find all sub-modules in the module, as well as their names.

    Say `self.children[4]['key'].sub_module` is a sub-module. Then the name will be
    `children[4]['key'].sub_module`. But if the sub-module is accessible at different
    paths, only one of the paths will be returned.
    """
    if type_ is None:
        type_ = BaseModule

    queue = deque([("self", self)])
    seen = {id(self)}

    def add_to_queue(name, item):
        if id(item) not in seen:
            seen.add(id(item))
            queue.append((name, item))

    while queue:
        name, item = queue.popleft()

        if isinstance(item, type_):
            yield name, item

        if isinstance(item, BaseModule):
            if skip_compiled and getattr(item, "_compiled", False):
                continue
            for sub_name, sub_item in item.__dict__.items():
                add_to_queue(f"{name}.{sub_name}", sub_item)

        elif isinstance(item, (list, tuple)):
            for i, sub_item in enumerate(item):
                add_to_queue(f"{name}[{i}]", sub_item)

        elif isinstance(item, dict):
            for key, sub_item in item.items():
                add_to_queue(f"{name}[{key}]", sub_item)

def parameters(self):
    return [param for _, param in self.named_parameters()]

def predictors(self):
    """Return all Predict modules in this module.

    Returns:
        list[Predict]: A list of all Predict instances in this module.

    Examples:
        >>> import dspy
        >>> class MyProgram(dspy.Module):
        ...     def __init__(self):
        ...         super().__init__()
        ...         self.qa = dspy.Predict("question -> answer")
        ...
        >>> program = MyProgram()
        >>> len(program.predictors())
        1
    """
    return [param for _, param in self.named_predictors()]

def reset_copy(self):
    """Deep copy the module and reset all parameters."""
    new_instance = self.deepcopy()

    for param in new_instance.parameters():
        param.reset()

    return new_instance

def save(self, path, save_program=False, modules_to_serialize=None):
    """Save the module.

    Save the module to a directory or a file. There are two modes:
    - `save_program=False`: Save only the state of the module to a json or pickle file, based on the value of
        the file extension.
    - `save_program=True`: Save the whole module to a directory via cloudpickle, which contains both the state and
        architecture of the model.

    If `save_program=True` and `modules_to_serialize` are provided, it will register those modules for serialization
    with cloudpickle's `register_pickle_by_value`. This causes cloudpickle to serialize the module by value rather
    than by reference, ensuring the module is fully preserved along with the saved program. This is useful
    when you have custom modules that need to be serialized alongside your program. If None, then no modules
    will be registered for serialization.

    We also save the dependency versions, so that the loaded model can check if there is a version mismatch on
    critical dependencies or DSPy version.

    Args:
        path (str): Path to the saved state file, which should be a .json or .pkl file when `save_program=False`,
            and a directory when `save_program=True`.
        save_program (bool): If True, save the whole module to a directory via cloudpickle, otherwise only save
            the state.
        modules_to_serialize (list): A list of modules to serialize with cloudpickle's `register_pickle_by_value`.
            If None, then no modules will be registered for serialization.

    """
    metadata = {}
    metadata["dependency_versions"] = get_dependency_versions()
    path = Path(path)

    if save_program:
        if path.suffix:
            raise ValueError(
                f"`path` must point to a directory without a suffix when `save_program=True`, but received: {path}"
            )
        if path.exists() and not path.is_dir():
            raise NotADirectoryError(f"The path '{path}' exists but is not a directory.")

        if not path.exists():
            # Create the directory (and any parent directories)
            path.mkdir(parents=True)
        logger.warning("Loading untrusted .pkl files can run arbitrary code, which may be dangerous. To avoid "
                      'this, prefer saving using json format using module.save("module.json").')
        try:
            with serialize_by_value(modules_to_serialize), open(path / "program.pkl", "wb") as f:
                cloudpickle.dump(self, f)
        except Exception as e:
            raise RuntimeError(
                f"Saving failed with error: {e}. Please remove the non-picklable attributes from your DSPy program, "
                "or consider using state-only saving by setting `save_program=False`."
            )
        with open(path / "metadata.json", "wb") as f:
            f.write(orjson.dumps(metadata, option=orjson.OPT_INDENT_2 | orjson.OPT_APPEND_NEWLINE))

        return

    if path.suffix == ".json":
        state = self.dump_state()
        state["metadata"] = metadata
        try:
            with open(path, "wb") as f:
                f.write(orjson.dumps(state, option=orjson.OPT_INDENT_2 | orjson.OPT_APPEND_NEWLINE))
        except Exception as e:
            raise RuntimeError(
                f"Failed to save state to {path} with error: {e}. Your DSPy program may contain non "
                "json-serializable objects, please consider saving the state in .pkl by using `path` ending "
                "with `.pkl`, or saving the whole program by setting `save_program=True`."
            )
    elif path.suffix == ".pkl":
        logger.warning("Loading untrusted .pkl files can run arbitrary code, which may be dangerous. To avoid "
                      'this, prefer saving using json format using module.save("module.json").')
        state = self.dump_state(json_mode=False)
        state["metadata"] = metadata
        with open(path, "wb") as f:
            cloudpickle.dump(state, f)
    else:
        raise ValueError(f"`path` must end with `.json` or `.pkl` when `save_program=False`, but received: {path}")

def set_lm(self, lm):
    """Set the language model for all predictors in this module.

    This method recursively sets the language model on every leaf module
    in this module.

    Args:
        lm: The language model instance to use for all predictors.

    Examples:
        >>> import dspy
        >>> lm = dspy.LM("openai/gpt-4o-mini")
        >>> program = dspy.Predict("question -> answer")
        >>> program.set_lm(lm)
    """
    for _, param in self.named_parameters():
        if isinstance(param, Module):
            param.lm = lm