docstr: Tests whether the NeRF model is initialized properly and can be trained.
docstr_tok: ["Tests","whether","the","NeRF","model","is","initialized","properly","and","can","be","trained","."]
code: def test_model_training(self):
      """Tests whether the NeRF model is initialized properly and can be trained."""
  
      app_network = model_lib.AppearanceNetwork(
          ray_samples_coarse=64,
          ray_samples_fine=64,
          near=1.0,
          far=3.0,
          n_freq_posenc_xyz=8,
          n_freq_posenc_dir=0,
          scene_bbox=(-1.0, -1.0, -1.0, 1.0, 1.0, 1.0),
          n_filters=128,
          num_latent_codes=4371,
          latent_code_dim=16,
          white_background=True,
          coarse_sampling_strategy="stratified",
      )
      app_network.init_model_and_codes()
      app_network.init_optimizer()
      app_network.init_checkpoint(checkpoint_dir="/tmp/")
  
      batch_size = 5
      n_rays = 128
  
      r_org = tf.zeros((batch_size, n_rays, 3), dtype=tf.float32)
      r_dir = tf.zeros((batch_size, n_rays, 3), dtype=tf.float32)
      shape_index = tf.zeros((batch_size), dtype=tf.int32)
      voxels = tf.zeros(
          (batch_size, 128, 128, 128, 1), dtype=tf.float32
      )
      w2v_alpha = tf.zeros((batch_size, 3), dtype=tf.float32)
      w2v_beta = tf.zeros((batch_size, 3), dtype=tf.float32)
      gt_rgb = tf.zeros((batch_size, n_rays, 3), dtype=tf.float32)
  
      loss = app_network.train_step(
          r_org, r_dir, shape_index, voxels, w2v_alpha, w2v_beta, gt_rgb
      )
  
      self.assertAllInRange(loss, 0.0, 1000.0)
  
imports: import tensorflow
  import tensorflow_graphics.projects.radiance_fields.sharf.appearance_net.model
  from tensorflow_graphics.util import test_case
label_desc: Use image node_id 1 for calling the ModelTest obj's underlying member method code with example usage: obj.test_model_training() without return types