input: Write a python function to plot a normal distribution curve given a mean and standard deviation using matplotlib and numpy.
  assert plot_normal_distribution(5, 1) is None  # This function generates a plot and returns None
  assert plot_normal_distribution(0, 2) is None  # This function generates a plot and returns None
output: def plot_normal_distribution(mean, std_dev):
      import matplotlib.pyplot as plt
      import numpy as np
  
      # Generate data for the normal distribution
      x = np.linspace(mean - 4*std_dev, mean + 4*std_dev, 1000)
      y = (1/(std_dev * np.sqrt(2 * np.pi))) * np.exp(-0.5 * ((x - mean) / std_dev)**2)
  
      # Plot the normal distribution curve
      plt.plot(x, y, label=f'Normal Distribution\nMean = {mean}, Std Dev = {std_dev}')
      plt.xlabel('x')
      plt.ylabel('Probability Density')
      plt.title('Normal Distribution Curve')
      plt.legend()
      plt.grid(True)
      plt.show()