text: import re
  from typing import Sequence, Tuple, Optional, Iterable
  
  import torch
  
  from allennlp.common.params import Params
  from allennlp.nn.regularizers.regularizer import Regularizer
  
  
  class RegularizerApplicator:
      """
      Applies regularizers to the parameters of a Module based on regex matches.
      """
      def __init__(self, regularizers: Sequence[Tuple[str, Regularizer]] = ()) -> None:
          """
          Parameters
          ----------
          regularizers : Sequence[Tuple[str, Regularizer]], optional (default = ())
              A sequence of pairs (regex, Regularizer), where each Regularizer
              applies to the parameters its regex matches (and that haven't previously
              been matched).
          """
          self._regularizers = regularizers
  
      def __call__(self, module: torch.nn.Module) -> torch.Tensor:
          """
          Parameters
          ----------
          module : torch.nn.Module, required
              The module to regularize.
          """
          accumulator = 0.0
          for name, parameter in module.named_parameters():
              # We first check if the parameter needs gradient updates or not
              if parameter.requires_grad:
                  # For each parameter find the first matching regex.
                  for regex, regularizer in self._regularizers:
                      if re.search(regex, name):
                          penalty = regularizer(parameter)
                          accumulator = accumulator + penalty
                          break
          return accumulator
  
      # Requires custom from_params because of complex logic.
      @classmethod
      def from_params(cls, params: Iterable[Tuple[str, Params]] = ()) -> Optional['RegularizerApplicator']:
          """
          Converts a List of pairs (regex, params) into an RegularizerApplicator.
          This list should look like
  
          [["regex1", {"type": "l2", "alpha": 0.01}], ["regex2", "l1"]]
  
          where each parameter receives the penalty corresponding to the first regex
          that matches its name (which may be no regex and hence no penalty).
          The values can either be strings, in which case they correspond to the names
          of regularizers, or dictionaries, in which case they must contain the "type"
          key, corresponding to the name of a regularizer. In addition, they may contain
          auxiliary named parameters which will be fed to the regularizer itself.
          To determine valid auxiliary parameters, please refer to the torch.nn.init documentation.
  
          Parameters
          ----------
          params : ``Params``, required.
              A Params object containing a "regularizers" key.
  
          Returns
          -------
          A RegularizerApplicator containing the specified Regularizers,
          or ``None`` if no Regularizers are specified.
          """
          if not params:
              return None
  
          instantiated_regularizers = []
          for parameter_regex, regularizer_params in params:
              if isinstance(regularizer_params, str):
                  regularizer = Regularizer.by_name(regularizer_params)()
              else:
                  regularizer_type = Regularizer.by_name(regularizer_params.pop("type"))
                  regularizer = regularizer_type(**regularizer_params)  # type: ignore
              instantiated_regularizers.append((parameter_regex, regularizer))
          return RegularizerApplicator(instantiated_regularizers)
  