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: Initialization function.
  
  Args:
    ckpt: str, path to checkpoint.
    in_grid_res: int, resolution of grid to feed to encoder.
    num_filters: int, number of base filters for encoder.
    codelen: int, length of output latent code.
    grid_batch: int, batch size of cut-out grid to evaluate at a time.
    gres: int, resolution of the full grid.
    overlap: bool, whether to do overlapping or non-overlapping cutout
      evaluations.
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code: def __init__(
      self,
      ckpt,
      in_grid_res=32,
      num_filters=32,
      codelen=128,
      grid_batch=128,
      gres=256,
      overlap=True,
  ):
      """Initialization function.
  
      Args:
        ckpt: str, path to checkpoint.
        in_grid_res: int, resolution of grid to feed to encoder.
        num_filters: int, number of base filters for encoder.
        codelen: int, length of output latent code.
        grid_batch: int, batch size of cut-out grid to evaluate at a time.
        gres: int, resolution of the full grid.
        overlap: bool, whether to do overlapping or non-overlapping cutout
          evaluations.
      """
      self.ckpt = ckpt
      self.codelen = codelen
      self.grid_batch = grid_batch
      self.in_grid_res = in_grid_res
      self.gres = gres
      self.num_filters = num_filters
      self.graph = tf.Graph()
      self._init_graph()
      self.global_step_ = self.global_step.eval(session=self.sess)
      if overlap:
          ijk = np.arange(
              0, gres - int(in_grid_res / 2), int(in_grid_res / 2)
          )
          self.out_grid_res = ijk.shape[0]
      else:
          ijk = np.arange(0, gres, in_grid_res)
          self.out_grid_res = ijk.shape[0]
      self.ijk = np.meshgrid(ijk, ijk, ijk, indexing="ij")
      self.ijk = np.stack(self.ijk, axis=-1).reshape([-1, 3])
  
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 FullGridEncoderEvaluator object from inherited base classes: object with example: obj = FullGridEncoderEvaluator(ckpt, in_grid_res, num_filters, codelen, grid_batch, gres, overlap)