Following the mathematical definition of a gaussian function :implement a function named gauss (x,mu, sigma,A,K) that takes X, Il, 0 ,A and K as argumentsand returns the values of g(x ).

Sure, here is an implementation of the gauss function in Python:

import numpy as np

def gauss(x, mu, sigma, A, K):
    """
    Returns the values of a Gaussian function for the given parameters.

    Parameters:
        x (ndarray): The input array of x values.
        mu (float): The mean of the Gaussian function.
        sigma (float): The standard deviation of the Gaussian function.
        A (float): The amplitude of the Gaussian function.
        K (float): The constant background value.

    Returns:
        ndarray: An array of y values corresponding to the input x values.
    """
    return A * np.exp(-(x - mu)**2 / (2 * sigma**2)) + K

You can use this function by passing in an array of x values, as well as the other parameters:

x = np.linspace(-10, 10, 100)
y = gauss(x, 0, 1, 1, 0)

This would return an array of y values for a Gaussian function with mean 0, standard deviation 1, amplitude 1, and background value 0, evaluated at 100 x values ranging from -10 to 10.