class_docstr: Prune model to an exact pruning level for one time.
class_docstr_tok: ["Prune","model","to","an","exact","pruning","level","for","one","time","."]
docstr: Parameters
  ----------
  model : torch.nn.Module
      Model to be pruned
  config_list : list
      List on pruning configs
code: def validate_config(self, model, config_list):
      """
      Parameters
      ----------
      model : torch.nn.Module
          Model to be pruned
      config_list : list
          List on pruning configs
      """
      schema = CompressorSchema(
          [
              {
                  "sparsity": And(float, lambda n: 0 < n < 1),
                  Optional("op_types"): [str],
                  Optional("op_names"): [str],
              }
          ],
          model,
          logger,
      )
  
      schema.validate(config_list)
  
imports: import logging
  from schema import And, Optional, SchemaError
  from .._graph_utils import TorchModuleGraph
  from ..utils.shape_dependency import ChannelDependency, GroupDependency
  from .constants import MASKER_DICT
  from ..utils.config_validation import CompressorSchema
  from ..compressor import Pruner
label_desc: Use image node_id 2 for calling the OneshotPruner obj's underlying member method code with example usage: obj.validate_config(model, config_list) without return types