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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 run_bottleneck_on_image(sess, image_data, image_data_tensor, decoded_image_tensor, resized_input_tensor, bottleneck_tensor):
 """Runs inference on an image to extract the 'bottleneck' summary layer. Args: sess: Current active TensorFlow Session. image_data: String of raw JPEG data. image_data_tensor: Input data layer in the graph. decoded_image_tensor: Output of initial image resizing and preprocessing. resized_input_tensor: The input node of the recognition graph. bottleneck_tensor: Layer before the final softmax. Returns: Numpy array of bottleneck values. """

  # First decode the JPEG image, resize it, and rescale the pixel values.
  resized_input_values = sess.run(decoded_image_tensor,
                                  {image_data_tensor: image_data})
  # Then run it through the recognition network.
  bottleneck_values = sess.run(bottleneck_tensor,
                               {resized_input_tensor: resized_input_values})
  bottleneck_values = np.squeeze(bottleneck_values)
  return bottleneck_values