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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 eval_image(image, height, width, scope=None):
 """Prepare one image for evaluation. Args: image: 3-D float Tensor height: integer width: integer scope: Optional scope for name_scope. Returns: 3-D float Tensor of prepared image. """

  with tf.name_scope(values=[image, height, width], name=scope,
                     default_name='eval_image'):
    # Crop the central region of the image with an area containing 87.5% of
    # the original image.
    image = tf.image.central_crop(image, central_fraction=0.875)

    # Resize the image to the original height and width.
    image = tf.expand_dims(image, 0)
    image = tf.image.resize_bilinear(image, [height, width],
                                     align_corners=False)
    image = tf.squeeze(image, [0])
    return image