class_docstr: Load pretrained UNet for generating feature grid for coarse voxel inputs.
class_docstr_tok: ["Load","pretrained","UNet","for","generating","feature","grid","for","coarse","voxel","inputs","."]
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.unet = g2g.UNet3D(
              in_grid_res=self.in_grid_res,
              out_grid_res=self.out_grid_res,
              num_filters=self.num_filters,
              max_filters=self.max_filters,
              out_features=self.out_features,
          )
          self.input_grid_ph = tf.placeholder(
              tf.float32, [None, None, None]
          )
          self.input_grid = self.input_grid_ph[
              tf.newaxis, ..., tf.newaxis
          ]
          self.feat_grid = self.unet(self.input_grid)
          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 UNetEvaluator obj's underlying member method code with example usage: obj._init_graph() without return types