class_docstr: Load pretrained refiner and evaluate for a given code.
    
class_docstr_tok: ["Load","pretrained","refiner","and","evaluate","for","a","given","code","."]
docstr: Initialize computation graph for tensorflow.
      
docstr_tok: ["Initialize","computation","graph","for","tensorflow","."]
code: def _init_graph(self):
      """Initialize computation graph for tensorflow."""
      with self.graph.as_default():
          self.refiner = im.ImNet(
              dim=self.dim,
              in_features=self.codelen,
              out_features=self.out_features,
              num_filters=self.num_filters,
          )
          self.global_step = tf.get_variable(
              "global_step", shape=[], dtype=tf.int64
          )
  
          self.pts_ph = tf.placeholder(
              tf.float32, shape=[self.point_batch, 3]
          )
          self.lat_ph = tf.placeholder(tf.float32, shape=[self.codelen])
  
          lat = tf.broadcast_to(
              self.lat_ph[tf.newaxis], [self.point_batch, self.codelen]
          )
          code = tf.concat((self.pts_ph, lat), axis=-1)  # [pb, 3+c]
  
          vals = self.refiner(code, training=False)  # [pb, 1]
          self.vals = tf.squeeze(vals, axis=1)  # [pb]
          self.saver = tf.train.Saver()
          self.sess = tf.Session()
          self.saver.restore(self.sess, self.ckpt)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import ast
  import numpy
  import tensorflow.compat.v1
  from tensorflow.compat.v1.io import gfile
  from tensorflow_graphics.projects.local_implicit_grid.core import implicit_nets
  from tensorflow_graphics.projects.local_implicit_grid.core import local_implicit_grid_layer
  from tensorflow_graphics.projects.local_implicit_grid.core import model_g2g
  from tensorflow_graphics.projects.local_implicit_grid.core import model_g2v
label_desc: Use image node_id 2 for calling the RefinerEvaluator obj's underlying member method code with example usage: obj._init_graph() without return types