class_docstr: Load pretrained refiner and evaluate for a given code.
    
class_docstr_tok: ["Load","pretrained","refiner","and","evaluate","for","a","given","code","."]
docstr: Evaluate network at locations specified by points.
  
  Args:
    lat: [self.codelen,] np array, latent code.
    points: [#v, self.dim] np array, point locations to evaluate.
  Returns:
    all_vals: [#v] np array, function values at locations.
docstr_tok: ["Evaluate","network","at","locations","specified","by","points",".","Args",":","lat",":","[","self.codelen",",","]","np","array",",","latent","code",".","points",":","[","#","v",",","self.dim","]","np","array",",","point","locations","to","evaluate",".","Returns",":","all_vals",":","[","#","v","]","np","array",",","function","values","at","locations","."]
code: def eval_points(self, lat, points):
      """Evaluate network at locations specified by points.
  
      Args:
        lat: [self.codelen,] np array, latent code.
        points: [#v, self.dim] np array, point locations to evaluate.
      Returns:
        all_vals: [#v] np array, function values at locations.
      """
      npt = points.shape[0]
      npb = int(np.ceil(float(npt) / self.point_batch))
      all_vals = np.zeros([npt], dtype=np.float32)
  
      for idx in range(npb):
          sid = int(idx * self.point_batch)
          eid = int(min(npt, sid + self.point_batch))
          pts = points[sid:eid]
          pad_w = self.point_batch - (eid - sid)
          pts = np.pad(pts, ((0, pad_w), (0, 0)), mode="constant")
          with self.graph.as_default():
              val = self.sess.run(
                  self.vals,
                  feed_dict={self.pts_ph: pts, self.lat_ph: lat},
              )
          all_vals[sid:eid] = val[: (eid - sid)]
      return all_vals
  
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 4 for calling the RefinerEvaluator obj's underlying member method code with example usage: obj.eval_points(lat, points) and returns: all_vals