class_docstr: Shape and appearance conditioned network for radiance fields.
class_docstr_tok: ["Shape","and","appearance","conditioned","network","for","radiance","fields","."]
docstr: NeRF-based network.
docstr_tok: ["NeRF-based","network","."]
code: def get_model(self):
      """NeRF-based network."""
      with tf.name_scope("Network/"):
          input_features = tf.keras.layers.Input(shape=[self.input_dim])
          feat0 = nerf_layers.concat_block(
              input_features, n_filters=self.n_filters, n_layers=5
          )
          feat1 = nerf_layers.dense_block(
              feat0, n_filters=self.n_filters, n_layers=3
          )
          rgb_density = tf.keras.layers.Dense(4)(feat1)
          return tf.keras.Model(
              inputs=[input_features], outputs=[rgb_density]
          )
  
imports: from absl import logging
  import numpy
  import tensorflow
  import tensorflow_graphics.geometry.representation.ray
  import tensorflow_graphics.math.feature_representation
  import tensorflow_graphics.projects.radiance_fields.nerf.layers
  import tensorflow_graphics.projects.radiance_fields.sharf.voxel_functions
  import tensorflow_graphics.projects.radiance_fields.utils
  import tensorflow_graphics.rendering.volumetric.ray_radiance
label_desc: Use image node_id 7 for calling the AppearanceNetwork obj's underlying member method code with example usage: obj.get_model() and returns: tf