class_docstr: Load pretrained grid encoder and evaluate a full input grid.
  
  Performs windowed encoding and outputs an encoded feature grid.
class_docstr_tok: ["Load","pretrained","grid","encoder","and","evaluate","a","full","input","grid",".","Performs","windowed","encoding","and","outputs","an","encoded","feature","grid","."]
docstr: Strided evaluation of full grid into feature grid.
  
  Args:
    grid: [gres, gres, gres] input feature grid.
  
  Returns:
    ogrid: [out_grid_res, out_grid_res, out_grid_res, codelen] output feature
    gird.
docstr_tok: ["Strided","evaluation","of","full","grid","into","feature","grid",".","Args",":","grid",":","[","gres",",","gres",",","gres","]","input","feature","grid",".","Returns",":","ogrid",":","[","out_grid_res",",","out_grid_res",",","out_grid_res",",","codelen","]","output","feature","gird","."]
code: def eval_grid(self, grid):
      """Strided evaluation of full grid into feature grid.
  
      Args:
        grid: [gres, gres, gres] input feature grid.
  
      Returns:
        ogrid: [out_grid_res, out_grid_res, out_grid_res, codelen] output feature
        gird.
      """
      # initialize output feature grid
      ogrid = np.zeros([self.ijk.shape[0], self.codelen])
      niters = np.ceil(self.ijk.shape[0] / self.grid_batch).astype(int)
      for idx in range(niters):
          sid = idx * self.grid_batch
          eid = min(sid + self.grid_batch, self.ijk.shape[0])
          start_ijk = self.ijk[sid:eid]
          # pad if last iteration does not have a full batch
          pad_w = self.grid_batch - start_ijk.shape[0]
          start_ijk = np.pad(
              start_ijk, ((0, pad_w), (0, 0)), mode="constant"
          )
          lats = self.sess.run(
              self.lats,
              feed_dict={self.grid_ph: grid, self.start_ph: start_ijk},
          )
          ogrid[sid:eid] = lats[: eid - sid]
      ogrid = ogrid.reshape(
          [
              self.out_grid_res,
              self.out_grid_res,
              self.out_grid_res,
              self.codelen,
          ]
      )
      return ogrid.astype(np.float32)
  
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 FullGridEncoderEvaluator obj's underlying member method code with example usage: obj.eval_grid(grid) and returns: ogrid