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 decompress_weights(
      self, u: tf.Tensor, sv: tf.Tensor
  ) -> tf.Tensor:
      return tf.matmul(u, sv)
  
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 3 for calling the SVD obj's underlying member method code with example usage: obj.decompress_weights(u, sv) and returns: tf