class_docstr: Quantizer using the DoReFa scheme, as defined in:
  Zhou et al., DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
  (https://arxiv.org/abs/1606.06160)
docstr: Parameters
  ----------
  model : torch.nn.Module
      Model to be pruned
  config_list : list of dict
      List of configurations
docstr_tok: ["Parameters","--","--","--","--","--","model",":","torch.nn.Module","Model","to","be","pruned","config_list",":","list","of","dict","List","of","configurations"]
code: def validate_config(self, model, config_list):
      """
      Parameters
      ----------
      model : torch.nn.Module
          Model to be pruned
      config_list : list of dict
          List of configurations
      """
      schema = CompressorSchema(
          [
              {
                  Optional("quant_types"): Schema(
                      [lambda x: x in ["weight"]]
                  ),
                  Optional("quant_bits"): Or(
                      And(int, lambda n: 0 < n < 32),
                      Schema(
                          {
                              Optional("weight"): And(
                                  int, lambda n: 0 < n < 32
                              )
                          }
                      ),
                  ),
                  Optional("op_types"): [str],
                  Optional("op_names"): [str],
              }
          ],
          model,
          logger,
      )
  
      schema.validate(config_list)
  
imports: import logging
  import copy
  import torch
  from schema import Schema, And, Or, Optional
  from ..utils.config_validation import CompressorSchema
  from ..compressor import Quantizer, QuantGrad, QuantType
label_desc: Use image node_id 2 for calling the DoReFaQuantizer obj's underlying member method code with example usage: obj.validate_config(model, config_list) without return types