text: from typing import List
  from logging import Logger, getLogger
  
  from numpy import ndarray, linspace
  from numpy.random import randn
  from freq_used.logging_utils import set_logging_basic_config
  from matplotlib import pyplot as plt
  from matplotlib.figure import Figure
  from matplotlib.axes import Axes
  
  from functions.unit_functions import sigmoid
  from functions.neural_network.simple_feedforward_neural_network import SimpleFeedforwardNeuralNetwork
  
  
  logger: Logger = getLogger()
  
  if __name__ == "__main__":
      set_logging_basic_config(__file__)
  
      x_min: float = -10.0
      x_max: float = 10.0
      num_plotting_points: int = 100
      num_plots: int = 10
  
      dim_list: List[int] = [1, 1000, 100, 1]
  
      x_array_1d: ndarray = linspace(x_min, x_max, num_plotting_points)
  
      figure: Figure
      axes: Axes
      figure, axes = plt.subplots()
      for idx in range(num_plots):
          weight_array_list: List[ndarray] = [
              5.0 * randn(dim_list[i] + 1, dim_list[i + 1]) for i in range(len(dim_list) - 1)
          ]
          simple_feed_forward_neural_network: SimpleFeedforwardNeuralNetwork = SimpleFeedforwardNeuralNetwork(
              weight_array_list, sigmoid
          )
          logger.info(simple_feed_forward_neural_network.get_shape_tuple())
          y_array_1d: ndarray = simple_feed_forward_neural_network.get_y_values_2d_from_x_values_1d(x_array_1d).ravel()
          axes.plot(x_array_1d, y_array_1d)
  
      figure.show()
  
      if "__file__" in dir():
          plt.show()
  