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: Initialize training weights for the compressible weight.
  
  It calls the `add_training_weight` to add a training weight for a given
  `pretrained_weight`. A `pretrained_weight` can have multiple training
  weights. We initialize the training weights for each compressible
  weight by just calling this function for each.
  
  Args:
    pretrained_weight: tf.Tensor of a pretrained weight of a layer that will
      be compressed eventually.
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code: def init_training_weights(self, pretrained_weight: tf.Tensor):
      """Initialize training weights for the compressible weight.
  
      It calls the `add_training_weight` to add a training weight for a given
      `pretrained_weight`. A `pretrained_weight` can have multiple training
      weights. We initialize the training weights for each compressible
      weight by just calling this function for each.
  
      Args:
        pretrained_weight: tf.Tensor of a pretrained weight of a layer that will
          be compressed eventually.
      """
  
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 2 for calling the WeightCompressor obj's underlying member method code with example usage: obj.init_training_weights(pretrained_weight) without return types