class_docstr: Class for using optimization to acquire feature grid.
class_docstr_tok: ["Class","for","using","optimization","to","acquire","feature","grid","."]
code: def _get_var_mapping(self, model, scope=""):
      vars_ = model.trainable_variables
      varnames = [v.name for v in vars_]  # .split(':')[0]
      varnames = [
          scope + v.replace("lig/", "").strip(":0") for v in varnames
      ]
      map_dict = dict(zip(varnames, vars_))
      return map_dict
  
imports: import os
  import numpy
  from skimage import measure
  import tensorflow.compat.v1
  from tensorflow_graphics.projects.local_implicit_grid.core import evaluator
  from tensorflow_graphics.projects.local_implicit_grid.core import local_implicit_grid_layer
  from tensorflow_graphics.projects.local_implicit_grid.core import point_utils
label_desc: Use image node_id 4 for calling the LIGOptimizer obj's underlying member method code with example usage: obj._get_var_mapping(model, scope) and returns: map_dict