class_docstr: Base class for all pruners which use a PruningMethod (implemented in
class_docstr_tok: ["Base","class","for","all","pruners","which","use","a","PruningMethod","(","implemented","in"]
docstr: Steps through the pruning schedule once. At every call to
  :meth:`.step`, an internal step counter is incremented by one.
docstr_tok: ["Steps","through","the","pruning","schedule","once",".","At","every","call","to",":","meth",":","`",".step","`",",","an","internal","step","counter","is","incremented","by","one","."]
code: def step(self):
      """
      Steps through the pruning schedule once. At every call to
      :meth:`.step`, an internal step counter is incremented by one.
      """
      raise NotImplementedError()
  
file_path: coremltools/coremltools/optimize/torch/pruning/_base_pruner.py
imports: import copy
  import logging
  from typing import Optional
  from typing import Tuple
  import torch
  from coremltools.optimize.torch._utils.torch_utils import get_eval_model
  from coremltools.optimize.torch.base_model_optimizer import BaseModelOptimizer
  from coremltools.optimize.torch.base_model_optimizer import _Report
  from coremltools.optimize.torch.optimization_config import OptimizationConfig
  from coremltools.optimize.torch.pruning._utils import get_global_sparsity_summaries
label_desc: Use image node_id 4 for calling the BasePrunerWithPruningMethod obj's underlying member method code with example usage: obj.step() without return types