class_docstr: Keras layer to clip box values to lie inside a given shape.
      
class_docstr_tok: ["Keras","layer","to","clip","box","values","to","lie","inside","a","given","shape","."]
code: def call(self, inputs, **kwargs):
      image, boxes = inputs
      shape = keras.backend.cast(
          keras.backend.shape(image), keras.backend.floatx()
      )
      if keras.backend.image_data_format() == "channels_first":
          _, _, height, width = tensorflow.unstack(shape, axis=0)
      else:
          _, height, width, _ = tensorflow.unstack(shape, axis=0)
  
      x1, y1, x2, y2 = tensorflow.unstack(boxes, axis=-1)
      x1 = tensorflow.clip_by_value(x1, 0, width - 1)
      y1 = tensorflow.clip_by_value(y1, 0, height - 1)
      x2 = tensorflow.clip_by_value(x2, 0, width - 1)
      y2 = tensorflow.clip_by_value(y2, 0, height - 1)
  
      return keras.backend.stack([x1, y1, x2, y2], axis=2)
  
imports: import tensorflow
  from tensorflow import keras
  from ..None import backend
  from ..utils import anchors
  import numpy
label_desc: Use image node_id 1 for calling the ClipBoxes obj's underlying member method code with example usage: obj.call(inputs) and returns: keras