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."""
  
      model = model_lib.NeRF(
          ray_samples_coarse=128,
          ray_samples_fine=128,
          near=1.0,
          far=6.0,
          scene_bbox=(-1.0, -1.0, -1.0, 1.0, 1.0, 1.0),
          n_freq_posenc_xyz=10,
          n_freq_posenc_dir=4,
          n_filters=128,
          white_background=True,
      )
      model.init_coarse_and_fine_models()
      model.init_optimizer(learning_rate=0.0001)
      model.init_checkpoint(checkpoint_dir="/tmp/")
  
      batch_size = 10
      n_rays = 256
  
      rays_org = tf.zeros((batch_size, n_rays, 3), dtype=tf.float32)
      rays_dir = tf.zeros((batch_size, n_rays, 3), dtype=tf.float32)
      pixels_rgb = tf.zeros((batch_size, n_rays, 3), dtype=tf.float32)
  
      rgb_loss = model.train_step(rays_org, rays_dir, pixels_rgb)
      self.assertAllInRange(rgb_loss, 0.0, 1000.0)
  
imports: from absl import flags
  import tensorflow
  import tensorflow_graphics.projects.radiance_fields.nerf.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