here is the example from chapter 3 (jupyter notebook), please modifiy the code below and explain what the code is doing?

from sklearn.datasets import fetch_mldata

mnist = fetch_mldata('MNIST original')

mnist

X, y = mnist["data"], mnist["target"]

X.shape

y.shape

28*28

%matplotlib inline

import matplotlib

import matplotlib.pyplot as plt

some_digit = X[36000]

some_digit_image = some_digit.reshape(28, 28)

plt.imshow(some_digit_image, cmap = matplotlib.cm.binary,

interpolation="nearest")

plt.axis("off")

save_fig("some_digit_plot")

plt.show()

def plot_digit(data):

image = data.reshape(28, 28)

plt.imshow(image, cmap = matplotlib.cm.binary,

interpolation="nearest")

plt.axis("off")

# EXTRA

def plot_digits(instances, images_per_row=10, **options):

size = 28

images_per_row = min(len(instances), images_per_row)

images = [instance.reshape(size,size) for instance in instances]

n_rows = (len(instances) - 1) // images_per_row + 1

row_images = []

n_empty = n_rows * images_per_row - len(instances)

images.append(np.zeros((size, size * n_empty)))

for row in range(n_rows):

rimages = images[row * images_per_row : (row + 1) * images_per_row]

row_images.append(np.concatenate(rimages, axis=1))

image = np.concatenate(row_images, axis=0)

plt.imshow(image, cmap = matplotlib.cm.binary, **options)

plt.axis("off")

plt.figure(figsize=(9,9))

example_images = np.r_[X[:12000:600], X[13000:30600:600], X[30600:60000:590]]

plot_digits(example_images, images_per_row=10)

save_fig("more_digits_plot")

plt.show()

y[36000]

X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]

import numpy as np

shuffle_index = np.random.permutation(60000)

X_train, y_train = X_train[shuffle_index], y_train[shuffle_index]

HL3: MINST Image Classification Points: 25 points Details: a. Start with MINST example in chapter 3 b. Write a function that can shift an MNIST image in any direction (left, right, up, or down) by one pixel Then, for each image in the training set, create four shifted copies (one per direction) and add them to the training set. Finally, train your best model on this expanded training set and measure its accuracy on the test set. You can use the shift() function from the scipy.ndimage.interpolation module. For example, shift(image, [2, 1], cval-0) shifts the image 2 pixels down and 1 pixel to the right c. Show the code running under Python 3 along with correct output. d. Compare the results between the original code and the one with the expanded dataset Please provide all source code, test cases, data files used, screen snapshots of vour code being compiled, running and provide a report with your analysis and explanation of your results. You may combine all files into a single PDF file Word document file or place all files into a single ZIP archiVe. HL3: MINST Image Classification Points: 25 points Details: a. Start with MINST example in chapter 3 b. Write a function that can shift an MNIST image in any direction (left, right, up, or down) by one pixel Then, for each image in the training set, create four shifted copies (one per direction) and add them to the training set. Finally, train your best model on this expanded training set and measure its accuracy on the test set. You can use the shift() function from the scipy.ndimage.interpolation module. For example, shift(image, [2, 1], cval-0) shifts the image 2 pixels down and 1 pixel to the right c. Show the code running under Python 3 along with correct output. d. Compare the results between the original code and the one with the expanded dataset Please provide all source code, test cases, data files used, screen snapshots of vour code being compiled, running and provide a report with your analysis and explanation of your results. You may combine all files into a single PDF file Word document file or place all files into a single ZIP archiVe