import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
# Visualizations will be shown in the notebook.
%matplotlib inline

methods = ['nearest', 'gaussian']
np.random.seed(0)

for samples in X_train():
    random_image = random.choice(samples)

fig, axes = plt.subplots(3, 6, figsize=(12, 6),
                     subplot_kw={'xticks': [], 'yticks': []})
fig.subplots_adjust(hspace=0.3, wspace=0.05)

for ax, interp_method in zip(axes.flat, methods):
    ax.imshow(random_image, interpolation=interp_method, cmap='viridis')
    ax.set_title(interp_method)

plt.imshow()