class_docstr: Evaluate sparse encoded feature grids.
class_docstr_tok: ["Evaluate","sparse","encoded","feature","grids","."]
code: def __init__(
      self,
      ckpt,
      num_filters,
      codelen,
      origin,
      grid_shape,
      part_size,
      overlap=True,
      scope="",
  ):
      self.scope = scope
      self.overlap = overlap
      self.ckpt = ckpt
      self.num_filters = num_filters
      self.codelen = codelen
      if overlap:
          self.res = (np.array(grid_shape) - 1) / 2.0
      else:
          self.res = np.array(grid_shape) - 1
      self.res = self.res.astype(np.int32)
      self.xmin = np.array(origin)
      self.xmax = self.xmin + self.res * part_size
      self.part_size = part_size
  
      self.lvg = LIGEvaluator(
          ckpt=ckpt,
          size=grid_shape,
          in_features=codelen,
          out_features=1,
          x_location_max=2 - float(overlap),
          num_filters=num_filters,
          min_grid_value=self.xmin,
          max_grid_value=self.xmax,
          net_type="imnet",
          method="linear" if overlap else "nn",
          scope=scope,
      )
  
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 SparseLIGEvaluator object from inherited base classes: object with example: obj = SparseLIGEvaluator(ckpt, num_filters, codelen, origin, grid_shape, part_size, overlap, scope)