class_docstr: Shape and appearance conditioned network for radiance fields.
class_docstr_tok: ["Shape","and","appearance","conditioned","network","for","radiance","fields","."]
docstr: Initialize the optimizers with a scheduler.
docstr_tok: ["Initialize","the","optimizers","with","a","scheduler","."]
code: def init_optimizer(
      self,
      learning_rate_network=0.0001,
      learning_rate_latent=0.01,
      decay_steps=10000,
      decay_rate=0.96,
      staircase=True,
  ):
      """Initialize the optimizers with a scheduler."""
      lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
          learning_rate_network,
          decay_steps=decay_steps,
          decay_rate=decay_rate,
          staircase=staircase,
      )
      self.optimizer_network = tf.keras.optimizers.Adam(
          learning_rate=lr_schedule
      )
      lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
          learning_rate_latent,
          decay_steps=decay_steps,
          decay_rate=decay_rate,
          staircase=staircase,
      )
      self.optimizer_latent = tf.keras.optimizers.Adam(
          learning_rate=lr_schedule
      )
  
imports: from absl import logging
  import numpy
  import tensorflow
  import tensorflow_graphics.geometry.representation.ray
  import tensorflow_graphics.math.feature_representation
  import tensorflow_graphics.projects.radiance_fields.nerf.layers
  import tensorflow_graphics.projects.radiance_fields.sharf.voxel_functions
  import tensorflow_graphics.projects.radiance_fields.utils
  import tensorflow_graphics.rendering.volumetric.ray_radiance
label_desc: Use image node_id 3 for calling the AppearanceNetwork obj's underlying member method code with example usage: obj.init_optimizer(learning_rate_network, learning_rate_latent, decay_steps, decay_rate, staircase) without return types