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 point sampling loss.
docstr_tok: ["Compute","point","sampling","loss","."]
code: def _compute_sample_loss(self, gt, output):
      """Compute point sampling loss."""
      sample_loss = tf.square(gt - output)
      if self._sample_bbx > 0:
          loss_bbx = sample_loss[:, : self._sample_bbx]
          loss_bbx = tf.reduce_mean(loss_bbx)
      else:
          loss_bbx = 0.0
      if self._sample_surf > 0:
          loss_surf = sample_loss[:, self._sample_bbx :]
          loss_surf = tf.reduce_mean(loss_surf)
      else:
          loss_surf = 0.0
      sample_loss = loss_bbx + 0.1 * loss_surf
      return sample_loss
  
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 6 for calling the MultiConvexNet obj's underlying member method code with example usage: obj._compute_sample_loss(gt, output) and returns: sample_loss