Implement the **Leaky ReLU activation function**, a small twist on ReLU that keeps a tiny gradient for negative inputs to help training stay stable. You’ll apply it element-wise to a vector of inputs from a basic deep learning forward pass.

$$
f(x) = \begin{cases} 
x & \text{if } x \ge 0 \\ 
\alpha x & \text{if } x < 0 
\end{cases}
$$

## Requirements

Implement the function
```python
import numpy as np

def leaky_relu(x, alpha=0.01):
    \"""
    Applies the Leaky ReLU activation element-wise.

    Leaky ReLU is defined as:
        f(x) = max(x, alpha * x)

    Args:
        x (np.ndarray): Input values (a 1D vector).
        alpha (float): Negative-slope coefficient.

    Returns:
        np.ndarray: Output vector after applying Leaky ReLU.
    \"""
    # Your solution below
```

**Rules:**
- Apply Leaky ReLU element-wise using: $$f(x) = \max(x, \alpha x)$$
- Use only NumPy and Python built-in libraries (no deep learning frameworks).
- Do not use any prebuilt activation helpers (e.g., from PyTorch/TensorFlow).
- Keep the function as a single forward-pass utility (no backprop needed).

## Example

```python
x = [-2.0, -1.0, 0.0, 3.0]
alpha = 0.1

leaky_relu(x, alpha)
```

Output:
```python
[-0.2, -0.1, 0.0, 3.0]
```

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Activation function leaky relu

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969. Activation function leaky relu

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Similar Questions

Argument	Type
x	np.ndarray
alpha	float