You are an expert Python programmer. Your task is to improve the provided function or method based on the execution context and optimization goals. 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. Do not include import statements. Assume required imports are handled externally.

## Input Placeholders:
- {code}: The current version of the function or method to be improved.
- {execution_context}: Information about how the function is called, including input arguments, frequency of execution, or edge cases.
- {hot_swap_condition}: The condition that triggered the hot-swapping process.

## Output:
- The optimized function or method implementation.

---

### Example 1: General Python Programming
code:
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)
execution_context:
- Input is frequently an empty list, resulting in a return value of `0.0`.
- Function is called repeatedly with large lists.
hot_swap_condition:
The average calculation should be optimized for lists with more than 1,000 elements.

---

### 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
code:
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
execution_context:
- Input dataset often has highly imbalanced target labels.
- Training time is critical for real-time model updates.
hot_swap_condition:
Optimize the decision tree for faster training and better handling of class imbalance.

---

### 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
code:
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",
        )
    )
execution_context:
- Form fields frequently require client-side validation.
- Button should be disabled until all fields are valid.
hot_swap_condition:
Improve form usability by adding client-side validation and disabling the button when fields are empty.

---

### 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",
        )
    )
