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: Batched slicing of original grid.
  
  Args:
    ary: tensor, rank = 3.
    start_ijk: [batch_size, 3] tensor, starting index.
    w: width of cube to extract.
    batch_size: int, batch size.
  
  Returns:
    batched_slices: [batch_size, w, w, w] tensor, batched slices of ary.
docstr_tok: ["Batched","slicing","of","original","grid",".","Args",":","ary",":","tensor",",","rank","=","3.","start_ijk",":","[","batch_size",",","3","]","tensor",",","starting","index",".","w",":","width","of","cube","to","extract",".","batch_size",":","int",",","batch","size",".","Returns",":","batched_slices",":","[","batch_size",",","w",",","w",",","w","]","tensor",",","batched","slices","of","ary","."]
code: def _batch_slice(self, ary, start_ijk, w, batch_size):
      """Batched slicing of original grid.
  
      Args:
        ary: tensor, rank = 3.
        start_ijk: [batch_size, 3] tensor, starting index.
        w: width of cube to extract.
        batch_size: int, batch size.
  
      Returns:
        batched_slices: [batch_size, w, w, w] tensor, batched slices of ary.
      """
      batch_size = start_ijk.shape[0]
      ijk = tf.range(w, dtype=tf.int32)
      slice_idx = tf.meshgrid(ijk, ijk, ijk, indexing="ij")
      slice_idx = tf.stack(
          slice_idx, axis=-1
      )  # [in_grid_res, in_grid_res, in_grid_res, 3]
      slice_idx = tf.broadcast_to(
          slice_idx[tf.newaxis], [batch_size, w, w, w, 3]
      )
      offset = tf.broadcast_to(
          start_ijk[:, tf.newaxis, tf.newaxis, tf.newaxis, :],
          [batch_size, w, w, w, 3],
      )
      slice_idx += offset
      # [batch_size, in_grid_res, in_grid_res, in_grid_res, 3]
      batched_slices = tf.gather_nd(ary, slice_idx)
      # [batch_size, in_grid_res, in_grid_res, in_grid_res]
      return batched_slices
  
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 FullGridEncoderEvaluator obj's underlying member method code with example usage: obj._batch_slice(ary, start_ijk, w, batch_size) and returns: batched_slices