class_docstr: Load pretrained refiner and evaluate for a given code.
    
class_docstr_tok: ["Load","pretrained","refiner","and","evaluate","for","a","given","code","."]
code: def __init__(
      self,
      ckpt,
      codelen,
      dim=3,
      out_features=1,
      num_filters=128,
      point_batch=20000,
  ):
      self.ckpt = ckpt
      self.codelen = codelen
      self.dim = dim
      self.out_features = out_features
      self.num_filters = num_filters
      self.point_batch = point_batch
      self.graph = tf.Graph()
      self._init_graph()
      self.global_step_ = self.global_step.eval(session=self.sess)
  
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 1 to create a new RefinerEvaluator object from inherited base classes: object with example: obj = RefinerEvaluator(ckpt, codelen, dim, out_features, num_filters, point_batch)