class_docstr: Load pretrained grid refiner and evaluate a feature grid.
    
class_docstr_tok: ["Load","pretrained","grid","refiner","and","evaluate","a","feature","grid","."]
docstr: Initialization function.
  
  Args:
    ckpt: str, path to checkpoint.
    size: list or tuple of ints, grid dimension in each dimension.
    in_features: int, number of input channels.
    out_features: int, number of output channels.
    x_location_max: float, relative coordinate range for one voxel.
    num_filters: int, number of filters for refiner.
    min_grid_value: tuple, lower bound of query points.
    max_grid_value: tuple, upper bound of query points.
    net_type: str, one of occnet/deepsdf.
    method: str, one of linear/nn.
    point_batch: int, pseudo batch size for evaluating points.
    scope: str, scope of imnet layer.
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code: def __init__(
      self,
      ckpt,
      size=(15, 15, 15),
      in_features=32,
      out_features=1,
      x_location_max=1,
      num_filters=32,
      min_grid_value=(0.0, 0.0, 0.0),
      max_grid_value=(1.0, 1.0, 1.0),
      net_type="imnet",
      method="linear",
      point_batch=20000,
      scope="",
  ):
      """Initialization function.
  
      Args:
        ckpt: str, path to checkpoint.
        size: list or tuple of ints, grid dimension in each dimension.
        in_features: int, number of input channels.
        out_features: int, number of output channels.
        x_location_max: float, relative coordinate range for one voxel.
        num_filters: int, number of filters for refiner.
        min_grid_value: tuple, lower bound of query points.
        max_grid_value: tuple, upper bound of query points.
        net_type: str, one of occnet/deepsdf.
        method: str, one of linear/nn.
        point_batch: int, pseudo batch size for evaluating points.
        scope: str, scope of imnet layer.
      """
      self.dim = 3  # hardcode for dim = 3
      self.ckpt = ckpt
      self.size = size
      self.x_location_max = x_location_max
      self.num_filters = num_filters
      self.in_features = in_features
      self.out_features = out_features
      self.net_type = net_type
      self.method = method
      self.point_batch = point_batch
      self.scope = scope
      self.min_grid_value = min_grid_value
      self.max_grid_value = max_grid_value
      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 LIGEvaluator object from inherited base classes: object with example: obj = LIGEvaluator(ckpt, size, in_features, out_features, x_location_max, num_filters, min_grid_value, max_grid_value, net_type, method, point_batch, scope)