class_docstr: Grid to vector model for part autoencoding.
class_docstr_tok: ["Grid","to","vector","model","for","part","autoencoding","."]
docstr: Initialization.
  
  Args:
    in_grid_res: int, input grid resolution, must be powers of 2.
    num_filters: int, number of feature layers at smallest grid resolution.
    codelen: int, length of local latent codes.
    name: str, name of the layer.
docstr_tok: ["Initialization",".","Args",":","in_grid_res",":","int",",","input","grid","resolution",",","must","be","powers","of","2.","num_filters",":","int",",","number","of","feature","layers","at","smallest","grid","resolution",".","codelen",":","int",",","length","of","local","latent","codes",".","name",":","str",",","name","of","the","layer","."]
code: def __init__(
      self,
      in_grid_res=32,
      num_filters=32,
      codelen=128,
      name="grid_encoder_vae",
  ):
      """Initialization.
  
      Args:
        in_grid_res: int, input grid resolution, must be powers of 2.
        num_filters: int, number of feature layers at smallest grid resolution.
        codelen: int, length of local latent codes.
        name: str, name of the layer.
      """
      super(GridEncoderVAE, self).__init__(name=name)
      self.in_grid_res = in_grid_res
      self.num_filters = num_filters
      self.codelen = codelen
  
      # number of input levels
      self.num_in_level = int(math.log(self.in_grid_res, 2))
  
      # create layers
      # feat. in downward path
      nd = [
          self.num_filters * (2**i)
          for i in range(self.num_in_level + 1)
      ]
  
      self.conv_in = layers.Conv3D(filters=nd[0], kernel_size=1)
      self.down_conv = [ResBlock3D(int(n / 2), n) for n in nd[1:]]
      self.down_pool = layers.MaxPool3D((2, 2, 2))
      self.fc_out = layers.Dense(self.codelen * 2)
  
file_path: graphics/tensorflow_graphics/projects/local_implicit_grid/core/model_g2v.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import tensorflow.compat.v1
label_desc: Use image node_id 1 to create a new GridEncoderVAE object from inherited base classes: layers with example: obj = GridEncoderVAE(in_grid_res, num_filters, codelen, name)