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 compress a single weight’s training form.
  
  'compress_training_weights' can refer to making the weight more amenable to
  compression or actually compress the weight.
  
  The default is an identity.
  
  Args:
    *training_weights: tf.Tensors representing all variables used during
      training, for a single compressible weight, in the order returned in
      `init_training_weights`.
  
  Returns:
    List of tf.Tensors to set to compressed or more compressible form.
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code: def compress_training_weights(
      self, *training_weights: tf.Tensor
  ) -> List[tf.Tensor]:
      """Define the operations to compress a single weight’s training form.
  
      'compress_training_weights' can refer to making the weight more amenable to
      compression or actually compress the weight.
  
      The default is an identity.
  
      Args:
        *training_weights: tf.Tensors representing all variables used during
          training, for a single compressible weight, in the order returned in
          `init_training_weights`.
  
      Returns:
        List of tf.Tensors to set to compressed or more compressible form.
      """
      return list(training_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 8 for calling the WeightCompressor obj's underlying member method code with example usage: obj.compress_training_weights() and returns: list