class_docstr: Define how to apply SVD algorithm.
  
  This periodic update and scheduling base SVD algorithm update the original
  weights to make lower rank by SVD for each update_freq steps. During the
  warmup steps, It adjust the rank from the original to target rank gradually.
class_docstr_tok: ["Define","how","to","apply","SVD","algorithm",".","This","periodic","update","and","scheduling","base","SVD","algorithm","update","the","original","weights","to","make","lower","rank","by","SVD","for","each","update_freq","steps",".","During","the","warmup","steps",",","It","adjust","the","rank","from","the","original","to","target","rank","gradually","."]
code: def init_training_weights(self, pretrained_weight: tf.Tensor):
      self.add_training_weight(
          name="w",
          shape=pretrained_weight.shape,
          dtype=pretrained_weight.dtype,
          initializer=tf.keras.initializers.Constant(pretrained_weight),
      )
      self.add_training_weight(
          name="step",
          shape=(),
          dtype=tf.int32,
          initializer=tf.keras.initializers.Constant(0),
      )
  
file_path: model-optimization/tensorflow_model_optimization/python/core/common/keras/compression/algorithms/periodical_update_and_scheduling.py
imports: from typing import List
  import tensorflow
  from tensorflow_model_optimization.python.core.common.keras.compression import algorithm
label_desc: Use image node_id 2 for calling the SVD obj's underlying member method code with example usage: obj.init_training_weights(pretrained_weight) without return types