class_docstr: Interface for weight compression algorithm that acts on a per-layer basis.
  
  This allows both options of either decompressing during inference or
  decompressing prior to inference (where compression occurs by applying a
  tool such as zip to the model file).
  
  This interface is a purely functional one.
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docstr: Define the operations to decompress a single weight’s compressed form.
  
  The default is an identity.
  
  Args:
     *compressed_weights: tf.Tensors representing a single weight’s compressed
       form, coming from what’s returned in `compress`.
  
  Returns:
    A tf.Tensor representing the decompressed `compressed_weights`.
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code: def decompress_weights(
      self, *compressed_weights: tf.Tensor
  ) -> tf.Tensor:
      """Define the operations to decompress a single weight’s compressed form.
  
      The default is an identity.
  
      Args:
         *compressed_weights: tf.Tensors representing a single weight’s compressed
           form, coming from what’s returned in `compress`.
  
      Returns:
        A tf.Tensor representing the decompressed `compressed_weights`.
      """
  
file_path: model-optimization/tensorflow_model_optimization/python/core/common/keras/compression/algorithm.py
imports: import abc
  from typing import List, Any
  import dataclasses
  import tensorflow
  from tensorflow_model_optimization.python.core.common.keras.compression.internal import optimize
label_desc: Use image node_id 9 for calling the WeightCompressor obj's underlying member method code with example usage: obj.decompress_weights() without return types