code: def clip_boxes(bboxes, imshape):
      """
      Clips bounding boxes to image boundaries based on image shape.
  
      Args:
          bboxes: Tensor with shape (num_bboxes, 4)
              where point order is x1, y1, x2, y2.
  
          imshape: Tensor with shape (2, )
              where the first value is height and the next is width.
  
      Returns
          Tensor with same shape as bboxes but making sure that none
          of the bboxes are outside the image.
      """
      with tf.name_scope("BoundingBoxTransform/clip_bboxes"):
          bboxes = tf.cast(bboxes, dtype=tf.float32)
          imshape = tf.cast(imshape, dtype=tf.float32)
  
          x1, y1, x2, y2 = tf.split(bboxes, 4, axis=1)
          width = imshape[1]
          height = imshape[0]
          x1 = tf.maximum(tf.minimum(x1, width - 1.0), 0.0)
          x2 = tf.maximum(tf.minimum(x2, width - 1.0), 0.0)
  
          y1 = tf.maximum(tf.minimum(y1, height - 1.0), 0.0)
          y2 = tf.maximum(tf.minimum(y2, height - 1.0), 0.0)
  
          bboxes = tf.concat([x1, y1, x2, y2], axis=1)
  
          return bboxes
  
imports: import tensorflow
label_desc: Use image node_id 4 for calling a global function with example usage: clip_boxes(bboxes, imshape) and returns: bboxes