code: def vanilla_theta_gradient(model_fn, batch_holder, hparams):
      """A vanilla gradient estimator for the pose, theta."""
      latent_holder, latent, occ_eval = model_fn(
          batch_holder, None, None, "gen_mesh"
      )
      if hparams.sample_vert > 0:
          points = batch_holder["point"]
          weights = batch_holder["weight"]
          n_vert = tf.shape(points)[2]
          sample_indices = tf.random.uniform(
              [1, 1, hparams.sample_vert],
              minval=0,
              maxval=n_vert,
              dtype=tf.int32,
          )
          points = tf.gather(
              points, sample_indices, axis=2, batch_dims=2
          )
          weights = tf.gather(
              weights, sample_indices, axis=2, batch_dims=2
          )
          batch_holder["point"] = points
          batch_holder["weight"] = weights
      unused_var0, unused_var1, occ = model_fn(
          batch_holder, None, None, "gen_mesh"
      )
      return (
          latent_holder,
          latent,
          occ_eval,
          tf.reduce_mean(tf.square(occ - hparams.level_set)),
      )
  
imports: from os import path
  import numpy
  from skimage import measure
  import tensorflow.compat.v1
  from tensorflow_graphics.projects.cvxnet.lib.libmise import mise
  from tensorflow_graphics.projects.nasa.lib import datasets
  from tensorflow_graphics.projects.nasa.lib import models
  import tensorflow_probability
  from tqdm import trange
  import trimesh
label_desc: Use image node_id 10 for calling a global function with example usage: vanilla_theta_gradient(model_fn, batch_holder, hparams) and returns: latent_holder,  latent,  occ_eval,  tf