class_docstr: Specifies when to prune layer and the sparsity(%) at each training step.
  
  PruningSchedule controls pruning during training by notifying at each step
  whether the layer's weights should be pruned or not, and the sparsity(%) at
  which they should be pruned.
  
  It can be invoked as a `callable` by providing the training `step` Tensor. It
  returns a tuple of bool and float tensors.
  
  ```python
    should_prune, sparsity = pruning_schedule(step)
  ```
  
  You can inherit this class to write your own custom pruning schedule.
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docstr: Instantiates a `PruningSchedule` from its config.
  
  Args:
      config: Output of `get_config()`.
  
  Returns:
      A `PruningSchedule` instance.
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code: def from_config(cls, config):
      """Instantiates a `PruningSchedule` from its config.
  
      Args:
          config: Output of `get_config()`.
  
      Returns:
          A `PruningSchedule` instance.
      """
      return cls(**config)
  
imports: import abc
  import six
  import tensorflow
label_desc: Use image node_id 6 for calling the PruningSchedule obj's underlying member method code with example usage: obj.from_config(cls, config) and returns: cls