new_contents: import numpy as np
  import tensorflow as tf
  import matplotlib.pyplot as plt
  from sklearn.datasets import load_sample_image
  from sklearn.datasets import load_sample_images
  # Utility functions
  def plot_image(image):
      plt.imshow(image, cmap="gray", interpolation="nearest")
      plt.axis("off")
  
  def plot_color_image(image):
      plt.imshow(image.astype(np.uint8),interpolation="nearest")
      plt.axis("off")
  
  # Load sample images
  china = load_sample_image('china.jpg')
  flower = load_sample_image('flower.jpg')
  image = china[150:220, 130:250]
  height, width, channels = image.shape
  image_grayscale = image.mean(axis=2).astype(np.float32)
  images = image_grayscale.reshape(1, height, width, 1)
  
  dataset = np.array(load_sample_images().images, dtype=np.float32)
  batchsize, height, width, channels = dataset.shape
  
  # Create 2 filters
  fmap = np.zeros(shape=(7, 7, channels, 2), dtype=np.float32)
  fmap[:, 3, 0, 0] = 1
  fmap[3, :, 0, 1] = 1
  plot_image(fmap[:,:,0,0])
  plt.show()
  plot_image(fmap[:,:,0,1])
  plt.show()
  
  X = tf.placeholder(tf.float32, shape=(None, height, width, channels))
  convolution = tf.nn.conv2d(X, fmap, strides=[1,2,2,1], padding='SAME')
  
  with tf.Session() as sess:
      output = sess.run(convolution, feed_dict={X: dataset})
  
  plt.imshow(output[0,:,:,1])
  plt.show()
subject: Add code for simple convnet