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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def image_to_tf_summary_value(image, tag):
 """Converts a NumPy image to a tf.Summary.Value object. Args: image: 3-D NumPy array. tag: name for tf.Summary.Value for display in tensorboard. Returns: image_summary: A tf.Summary.Value object. """

  curr_image = np.asarray(image, dtype=np.uint8)
  height, width, n_channels = curr_image.shape
  # If monochrome image, then reshape to [height, width]
  if n_channels == 1:
    curr_image = np.reshape(curr_image, [height, width])
  s = io.BytesIO()
  matplotlib_pyplot().imsave(s, curr_image, format="png")
  img_sum = tf.Summary.Image(encoded_image_string=s.getvalue(),
                             height=height, width=width,
                             colorspace=n_channels)
  return tf.Summary.Value(tag=tag, image=img_sum)