#### Scaled Exponential Linear Unit

p
```"""
Implements the Scaled Exponential Linear Unit or SELU function.
The function takes a vector of K real numbers and two real numbers
alpha(default = 1.6732) & lambda (default = 1.0507) as input and
then applies the SELU function to each element of the vector.
SELU is a self-normalizing activation function. It is a variant
of the ELU. The main advantage of SELU is that we can be sure
that the output will always be standardized due to its
self-normalizing behavior. That means there is no need to
include Batch-Normalization layers.
References :
https://iq.opengenus.org/scaled-exponential-linear-unit/
"""

import numpy as np

def scaled_exponential_linear_unit(
vector: np.ndarray, alpha: float = 1.6732, lambda_: float = 1.0507
) -> np.ndarray:
"""
Applies the Scaled Exponential Linear Unit function to each element of the vector.
Parameters :
vector : np.ndarray
alpha : float (default = 1.6732)
lambda_ : float (default = 1.0507)

Returns : np.ndarray
Formula : f(x) = lambda_ * x if x > 0
lambda_ * alpha * (e**x - 1) if x <= 0
Examples :
>>> scaled_exponential_linear_unit(vector=np.array([1.3, 3.7, 2.4]))
array([1.36591, 3.88759, 2.52168])

>>> scaled_exponential_linear_unit(vector=np.array([1.3, 4.7, 8.2]))
array([1.36591, 4.93829, 8.61574])
"""
return lambda_ * np.where(vector > 0, vector, alpha * (np.exp(vector) - 1))

if __name__ == "__main__":
import doctest

doctest.testmod()
```