You are an expert Python programmer. Your task is to improve the provided function or method. Follow these rules:
1. Retain the original functionality as described in the docstring and inline comments.
2. Focus on improving efficiency, readability, and maintainability.
3. Address specific performance bottlenecks or redundant operations based on the execution context.
4. Retain import statements inside the function/method if needed.


### Example 1: General Python Programming
## Current version of the function or method to be improved:
def calculate_average(numbers):
    """
    Calculates the average of a list of numbers.

    Args:
        numbers (list of float): A list of numeric values.

    Returns:
        float: The average of the list.
    """
    if not numbers:
        return 0.0
    return sum(numbers) / len(numbers)

### Optimized Implementation:
def calculate_average(numbers):
    """
    Calculates the average of a list of numbers.

    Args:
        numbers (list of float): A list of numeric values.

    Returns:
        float: The average of the list.
    """
    # Return early for an empty list
    if not numbers:
        return 0.0

    # Use a generator expression for memory efficiency on large lists
    total = sum(n for n in numbers)
    return total / len(numbers)

---

### Example 2: Machine Learning with Scikit-learn
## Current version of the function or method to be improved:
def train_decision_tree(X, y):
    """
    Trains a decision tree classifier.

    Args:
        X (numpy.ndarray): Feature matrix.
        y (numpy.ndarray): Target labels.

    Returns:
        sklearn.tree.DecisionTreeClassifier: The trained decision tree model.
    """
    from sklearn.tree import DecisionTreeClassifier
    model = DecisionTreeClassifier()
    model.fit(X, y)
    return model

### Optimized Implementation:
def train_decision_tree(X, y):
    """
    Trains a decision tree classifier.

    Args:
        X (numpy.ndarray): Feature matrix.
        y (numpy.ndarray): Target labels.

    Returns:
        sklearn.tree.DecisionTreeClassifier: The trained decision tree model.
    """
    from sklearn.tree import DecisionTreeClassifier
    from sklearn.utils.class_weight import compute_class_weight
    import numpy as np

    # Compute class weights for balancing
    classes = np.unique(y)
    class_weights = compute_class_weight('balanced', classes=classes, y=y)

    # Train the decision tree with class weights
    model = DecisionTreeClassifier(class_weight=dict(zip(classes, class_weights)))
    model.fit(X, y)
    return model

---

### Example 3: Web Programming with Reflex
## Current version of the function or method to be improved:
def create_signup_form():
    """
    Creates a signup form using Reflex.

    Returns:
        rx.Component: A Reflex component for the signup form.
    """
    return rx.card(
        rx.vstack(
            rx.text("Email", size="3"),
            rx.input(placeholder="Enter your email", type="email"),
            rx.text("Password", size="3"),
            rx.input(placeholder="Enter your password", type="password"),
            rx.button("Register", size="3"),
            spacing="4",
        )
    )

### Optimized Implementation:
def create_signup_form():
    """
    Creates a signup form using Reflex.

    Returns:
        rx.Component: A Reflex component for the signup form.
    """
    return rx.card(
        rx.vstack(
            rx.text("Email", size="3"),
            rx.input(placeholder="Enter your email", type="email", on_change=State.validate_email),
            rx.text("Password", size="3"),
            rx.input(placeholder="Enter your password", type="password", on_change=State.validate_password),
            rx.button(
                "Register",
                size="3",
                is_disabled=not State.is_form_valid,
                on_click=State.submit_form,
            ),
            spacing="4",
        )
    )
    
## Current version of the function or method to be improved:
{code}  

### Optimized Implementation:  

