class_docstr: Shape auto-encoder with multiple convex polytopes.
  
  Attributes:
    n_params: int, the number of hyperplane parameters.
class_docstr_tok: ["Shape","auto-encoder","with","multiple","convex","polytopes",".","Attributes",":","n_params",":","int",",","the","number","of","hyperplane","parameters","."]
docstr: Compute bounding box loss.
docstr_tok: ["Compute","bounding","box","loss","."]
code: def _compute_bbx_loss(self, trans, pts, gt):
      """Compute bounding box loss."""
      oo = 1e5
      inside = tf.expand_dims(tf.cast(gt > 0.5, tf.float32), axis=1)
      trans = tf.expand_dims(trans, axis=2)
      pts = tf.expand_dims(pts, axis=1)
      distances = tf.reduce_sum(
          tf.square(trans - pts), axis=-1, keepdims=True
      )
      distances = inside * distances + (1 - inside) * oo
      min_dis = tf.reduce_min(distances, axis=2)
      return tf.reduce_mean(min_dis)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import tensorflow.compat.v1
  from tensorflow_graphics.projects.cvxnet.lib import resnet
label_desc: Use image node_id 7 for calling the MultiConvexNet obj's underlying member method code with example usage: obj._compute_bbx_loss(trans, pts, gt) and returns: tf