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 a piece of the forward pass during training.
  
  It operates on a single compressible weight.
  The default throws an error when training occurs.
  
  Args:
     *training_weights: tf.Tensors representing any variables used during
       training, for a single compressible weight, in the order returned in
       `init_training_weights`.
  
  Returns:
     tf.Tensor to set the compressible weight to.
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code: def project_training_weights(
      self, *training_weights: tf.Tensor
  ) -> tf.Tensor:
      """Define a piece of the forward pass during training.
  
      It operates on a single compressible weight.
      The default throws an error when training occurs.
  
      Args:
         *training_weights: tf.Tensors representing any variables used during
           training, for a single compressible weight, in the order returned in
           `init_training_weights`.
  
      Returns:
         tf.Tensor to set the compressible weight to.
      """
  
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 4 for calling the WeightCompressor obj's underlying member method code with example usage: obj.project_training_weights() without return types