class_docstr: MLP decoder to decode latent codes to hyperplane parameters.
class_docstr_tok: ["MLP","decoder","to","decode","latent","codes","to","hyperplane","parameters","."]
code: def __init__(self, dims):
      super(Decoder, self).__init__()
      self._decoder = keras.Sequential()
      layer_sizes = [1024, 1024, 2048]
      for layer_size in layer_sizes:
          self._decoder.add(
              keras.layers.Dense(
                  layer_size, activation=tf.nn.leaky_relu
              )
          )
      self._decoder.add(keras.layers.Dense(dims, activation=None))
  
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 1 to create a new Decoder object from inherited base classes: keras.layers with example: obj = Decoder(dims)