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: Decode the hyperplane parameters into indicator fuctions.
  
  Args:
    params: Tensor, [batch_size, n_params], hyperplane parameters.
    points: Tensor, [batch_size, n_points, dims], query points.
    training: bool, use training mode if true.
  
  Returns:
    indicator: Tensor, [batch_size, n_points, 1], indicators for query points.
    extra: list, contains:
      trans: Tensor, [batch_size, n_parts, dims], translations.
      imgsum: Tensor, [batch_size, n_points, 1], sum of indicators.
      offset: Tensor, [batch_size, n_parts, n_half_planes, 1], off set of
      hyperplanes.
      image_indica: Tensor, [batch_Size, n_parts, n_points, 1], per part
      indicators.
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code: def decode(self, params, points, training):
      """Decode the hyperplane parameters into indicator fuctions.
  
      Args:
        params: Tensor, [batch_size, n_params], hyperplane parameters.
        points: Tensor, [batch_size, n_points, dims], query points.
        training: bool, use training mode if true.
  
      Returns:
        indicator: Tensor, [batch_size, n_points, 1], indicators for query points.
        extra: list, contains:
          trans: Tensor, [batch_size, n_parts, dims], translations.
          imgsum: Tensor, [batch_size, n_points, 1], sum of indicators.
          offset: Tensor, [batch_size, n_parts, n_half_planes, 1], off set of
          hyperplanes.
          image_indica: Tensor, [batch_Size, n_parts, n_points, 1], per part
          indicators.
      """
      theta, translations, blend_terms = self._split_params(params)
      indicator, (trans, imgsum, offset, image_indica) = self.sdf(
          theta, translations, blend_terms, points
      )
      return indicator, (trans, imgsum, offset, image_indica)
  
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 4 for calling the MultiConvexNet obj's underlying member method code with example usage: obj.decode(params, points, training) and returns: indicator,  trans,  imgsum,  offset,  image_indica