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: Encode the input image into hyperplane parameters.
  
  Args:
    img: Tensor, [batch_size, height, width, channels], input image.
    training: bool, use training mode if true.
  
  Returns:
    params: Tensor, [batch_size, n_params], hyperplane parameters.
docstr_tok: ["Encode","the","input","image","into","hyperplane","parameters",".","Args",":","img",":","Tensor",",","[","batch_size",",","height",",","width",",","channels","]",",","input","image",".","training",":","bool",",","use","training","mode","if","true",".","Returns",":","params",":","Tensor",",","[","batch_size",",","n_params","]",",","hyperplane","parameters","."]
code: def encode(self, img, training):
      """Encode the input image into hyperplane parameters.
  
      Args:
        img: Tensor, [batch_size, height, width, channels], input image.
        training: bool, use training mode if true.
  
      Returns:
        params: Tensor, [batch_size, n_params], hyperplane parameters.
      """
      x = self.img_encoder(img, training=training)
      return self.beta_decoder(x, training=training)
  
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 3 for calling the MultiConvexNet obj's underlying member method code with example usage: obj.encode(img, training) and returns: self