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 balance loss.
docstr_tok: ["Compute","balance","loss","."]
code: def _compute_balance_loss(self, indicas, labels):
      """Compute balance loss."""
      indicas = indicas * tf.expand_dims(labels, axis=1)
      indicas = tf.squeeze(
          indicas, axis=-1
      )  # Have to squeeze it for top_k
      vals, unused_var = tf.nn.top_k(indicas, k=self._k)
      return tf.reduce_mean(tf.square(1.0 - vals))
  
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 9 for calling the MultiConvexNet obj's underlying member method code with example usage: obj._compute_balance_loss(indicas, labels) and returns: tf