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"]
code: def _is_prepared(self) -> bool:
      return len(self._pruner_info) > 0
  
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 2 for calling the BasePrunerWithPruningMethod obj's underlying member method code with example usage: obj._is_prepared() and returns: unknown