class_docstr: UNet that consumes even dimension grid and outputs odd dimension grid.
class_docstr_tok: ["UNet","that","consumes","even","dimension","grid","and","outputs","odd","dimension","grid","."]
code: def _create_layers(self):
      num_filter_down = [
          self.num_filters * (2 ** (i + 1))
          for i in range(self.num_in_level)
      ]
      # num. features in downward path
      num_filter_down = [
          n if n <= self.max_filters else self.max_filters
          for n in num_filter_down
      ]
      num_filter_up = num_filter_down[::-1][1 : self.num_out_level]
      self.num_filter_down = num_filter_down
      self.num_filter_up = num_filter_up
      self.conv_in = ResBlock3D(self.num_filters, self.num_filters)
      self.conv_out = ResBlock3D(
          self.out_features, self.out_features, final_relu=False
      )
      self.down_modules = [
          ResBlock3D(int(n / 2), n) for n in num_filter_down
      ]
      self.up_modules = [ResBlock3D(n, n) for n in num_filter_up]
      self.dnpool = layers.MaxPool3D((2, 2, 2))
      self.upsamp = layers.UpSampling3D((2, 2, 2))
      self.up_final = layers.UpSampling3D((2, 2, 2))
  
imports: import math
  import tensorflow.compat.v1
  from tensorflow_graphics.projects.local_implicit_grid.core import local_implicit_grid_layer
label_desc: Use image node_id 2 for calling the UNet3DOdd obj's underlying member method code with example usage: obj._create_layers() without return types