class_docstr: Original NeRF network.
class_docstr_tok: ["Original","NeRF","network","."]
docstr: Constructs the original NeRF network as a keras model.
docstr_tok: ["Constructs","the","original","NeRF","network","as","a","keras","model","."]
code: def get_model(self):
      """Constructs the original NeRF network as a keras model."""
      with tf.name_scope("Network/"):
          xyz_features = tf.keras.layers.Input(
              shape=[None, None, self.xyz_dim]
          )
          dir_features = tf.keras.layers.Input(
              shape=[None, None, self.dir_dim]
          )
  
          feat0 = nerf_layers.concat_block(
              xyz_features, n_filters=self.n_filters, n_layers=4
          )
          feat1 = nerf_layers.dense_block(
              feat0, n_filters=self.n_filters, n_layers=4
          )
          feat2 = tf.keras.layers.Dense(self.n_filters)(feat1)
          density = tf.keras.layers.Dense(1)(feat2)
          feat2_dir = tf.keras.layers.concatenate(
              [feat2, dir_features], -1
          )
          feat3 = tf.keras.layers.Dense(self.n_filters // 2)(feat2_dir)
          rgb = tf.keras.layers.Dense(3)(feat3)
          rgb_density = tf.keras.layers.concatenate([rgb, density], -1)
          return tf.keras.Model(
              inputs=[xyz_features, dir_features], outputs=[rgb_density]
          )
  
file_path: graphics/tensorflow_graphics/projects/radiance_fields/nerf/model.py
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.utils
  import tensorflow_graphics.rendering.volumetric.ray_radiance
label_desc: Use image node_id 6 for calling the NeRF obj's underlying member method code with example usage: obj.get_model() and returns: tf