import tensorflow as tf


    Returns:
      A transformed tensor (tf.float32).

    """
    with tf.variable_scope('_interpolate'):
      num_batch = im.get_shape().as_list()[0]
      depth = im.get_shape().as_list()[1]
      height = im.get_shape().as_list()[2]
      width = im.get_shape().as_list()[3]
      channels = im.get_shape().as_list()[4]

      x = tf.to_float(x)
      y = tf.to_float(y)
      z = tf.to_float(z)
      depth_f = tf.to_float(depth)
      height_f = tf.to_float(height)
      width_f = tf.to_float(width)
      # Number of disparity interpolated.
      out_depth = out_size[0]
      out_height = out_size[1]
      out_width = out_size[2]
      zero = tf.zeros([], dtype='int32')
      # 0 <= z < depth, 0 <= y < height & 0 <= x < width.
      max_z = tf.to_int32(tf.shape(im)[1] - 1)
      max_y = tf.to_int32(tf.shape(im)[2] - 1)
      max_x = tf.to_int32(tf.shape(im)[3] - 1)

