class_docstr: Load pretrained grid refiner and evaluate a feature grid.
    
class_docstr_tok: ["Load","pretrained","grid","refiner","and","evaluate","a","feature","grid","."]
code: def _get_var_mapping(self, model):
      vars_ = model.trainable_variables
      varnames = [v.name for v in vars_]  # .split(':')[0]
      varnames = [
          self.scope + v.replace("lig/", "").strip(":0")
          for v in varnames
      ]
      map_dict = dict(zip(varnames, vars_))
      return map_dict
  
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 6 for calling the LIGEvaluator obj's underlying member method code with example usage: obj._get_var_mapping(model) and returns: map_dict