arrayA = ([[1, 2],                  [3, 4]]) arrayB = ([[5, 6, 7],                  [8, 9, 10]]) python numpy code to multiply array a and b to get result below arrayB = ([[5, 12, 7],                  [24, 36, 10

Prompt: In Python language, write a function that applies the logistic sigmoid function to all elements of a NumPy array. Code:   import numpy as np import math import matplotlib.pyplot as plt import pandas as pd   def sigmoid(inputArray):               modifiedArray = np.zeros(len(inputArray))               #YOUR CODE HERE:               return(modifiedArray) def test(): inputs = np.arange(-100, 100, 0.5) outputs = sigmoid(inputs) plt.figure(1) plt.plot(inputs) plt.title('Input') plt.xlabel('Index') plt.ylabel('Value') plt.show() plt.figure(2) plt.plot(outputs,'Black') plt.title('Output') plt.xlabel('Index') plt.ylabel('Value') plt.show() test()

Python Please **Bold is code** Recap of two-dimensional arrays and their numpy implementation  import numpy as np my_2d_array = np.array([[1,2],[3,4],[5,6]]) print("This is my_2d_array:") print(my_2d_array) print("This array has shape " + str(my_2d_array.shape) + " as there are " + str(my_2d_array.shape[0]) +  " rows and " + str(my_2d_array.shape[1]) + " columns.") print("The entry of the array with row index " + str(1) + " and column index " + str(1) + " has value " + str(my_2d_array[1,1])) 1) In this problem, implement a TwoDArray class that is meant to mimic (some of) the functionality of a two-dimensional numpy array. DO NOT use numpy at any point.  1A) Give the class an __init__ method Make sure that the variable array is a valid input. This means you should a) Make sure that array is a list of lists. and b) Make sure that each of the inner lists has the same length. You do not need to check for anything else. Rise the appropriate error(s) if these conditions are not met. It is up…

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