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.
class_docstr_tok: ["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","."]
code: def _validate_sparsity(self, sparsity, variable_name):
      if not 0.0 <= sparsity < 1.0:
          raise ValueError(
              "{} must be in range [0,1)".format(variable_name)
          )
  
imports: import abc
  import six
  import tensorflow
label_desc: Use image node_id 3 for calling the PruningSchedule obj's underlying member method code with example usage: obj._validate_sparsity(sparsity, variable_name) without return types