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","."]
code: def __init__(
      self,
      ckpt,
      in_grid_res,
      out_grid_res,
      num_filters,
      max_filters,
      out_features,
      sph_norm=0.0,
  ):
      self.ckpt = ckpt
      self.in_grid_res = in_grid_res
      self.out_grid_res = out_grid_res
      self.num_filters = num_filters
      self.max_filters = max_filters
      self.out_features = out_features
      self.sph_norm = sph_norm
      self.graph = tf.Graph()
      self._init_graph()
  
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 1 to create a new UNetEvaluator object from inherited base classes: object with example: obj = UNetEvaluator(ckpt, in_grid_res, out_grid_res, num_filters, max_filters, out_features, sph_norm)