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."""
  
      batch_size = 10
      latent_code_dim = 256
  
      geom_network = model_lib.GeometryNetwork(
          num_latent_codes=4371,
          latent_code_dim=latent_code_dim,
          fc_channels=512,
          fc_activation="relu",
          conv_size=4,
          norm3d="batchnorm",
          bce_gamma=0.8,
          proj_weight=0.01,
          mirror_weight=1.0,
      )
      geom_network.init_model()
      geom_network.init_optimizer()
      geom_network.init_checkpoint(checkpoint_dir="/tmp/")
  
      latent_codes = tf.zeros((batch_size, latent_code_dim))
      pred_logits_voxels = geom_network.model(latent_codes)
      pred_voxels = tf.sigmoid(pred_logits_voxels)
      self.assertAllInRange(pred_voxels, 0.0, 1.0)
  
imports: from absl import flags
  import tensorflow
  import tensorflow_graphics.projects.radiance_fields.sharf.geometry_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