class_docstr: Load pretrained UNet for generating feature grid for coarse voxel inputs.
class_docstr_tok: ["Load","pretrained","UNet","for","generating","feature","grid","for","coarse","voxel","inputs","."]
docstr: Evaluate input grid (no batching).
  
  Args:
    input_grid: [in_grid_res, in_grid_res, in_grid_res] tensor.
  Returns:
    [out_grid_res, out_grid_res, out_grid_res, out_features]
docstr_tok: ["Evaluate","input","grid","(","no","batching",")",".","Args",":","input_grid",":","[","in_grid_res",",","in_grid_res",",","in_grid_res","]","tensor",".","Returns",":","[","out_grid_res",",","out_grid_res",",","out_grid_res",",","out_features","]"]
code: def eval_grid(self, input_grid):
      """Evaluate input grid (no batching).
  
      Args:
        input_grid: [in_grid_res, in_grid_res, in_grid_res] tensor.
      Returns:
        [out_grid_res, out_grid_res, out_grid_res, out_features]
      """
      with self.graph.as_default():
          feat_grid = self.sess.run(
              self.feat_grid, feed_dict={self.input_grid_ph: input_grid}
          )
      feat_grid = feat_grid[0]
      if self.sph_norm > 0:
          feat_grid = (
              feat_grid
              / np.linalg.norm(feat_grid, axis=-1, keepdims=True)
              * self.sph_norm
          )
      return feat_grid
  
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 3 for calling the UNetEvaluator obj's underlying member method code with example usage: obj.eval_grid(input_grid) and returns: feat_grid