class_docstr: Load pretrained grid refiner and evaluate a feature grid.
    
class_docstr_tok: ["Load","pretrained","grid","refiner","and","evaluate","a","feature","grid","."]
docstr: Evaluate network on a grid.
  
  Args:
    latgrid: [size0, size1, size2, self.codelen] np array, latent code.
    xmin: float, minimum coordinate value for grid.
    xmax: float, maximum coordinate value for grid.
    res: int, resolution (per dimension) of grid.
  Returns:
    grid_val: [res, res, res] np.float32 array, grid of values from query.
docstr_tok: ["Evaluate","network","on","a","grid",".","Args",":","latgrid",":","[","size0",",","size1",",","size2",",","self.codelen","]","np","array",",","latent","code",".","xmin",":","float",",","minimum","coordinate","value","for","grid",".","xmax",":","float",",","maximum","coordinate","value","for","grid",".","res",":","int",",","resolution","(","per","dimension",")","of","grid",".","Returns",":","grid_val",":","[","res",",","res",",","res","]","np.float32","array",",","grid","of","values","from","query","."]
code: def eval_grid(self, latgrid, xmin=0.0, xmax=1.0, res=128):
      """Evaluate network on a grid.
  
      Args:
        latgrid: [size0, size1, size2, self.codelen] np array, latent code.
        xmin: float, minimum coordinate value for grid.
        xmax: float, maximum coordinate value for grid.
        res: int, resolution (per dimension) of grid.
      Returns:
        grid_val: [res, res, res] np.float32 array, grid of values from query.
      """
      grid_points = self._get_grid_points(xmin=xmin, xmax=xmax, res=res)
      point_val = self.eval_points(latgrid, grid_points)
      grid_val = point_val.reshape([res, res, res])
      return grid_val
  
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 5 for calling the LIGEvaluator obj's underlying member method code with example usage: obj.eval_grid(latgrid, xmin, xmax, res) and returns: grid_val