class_docstr: Class which converts the supplied pytorch model into an equivalent model
  which uses abstract operations
docstr: After an update on the convolutional weights, re-convert weights into the equivalent dense layer.
docstr_tok: ["After","an","update","on","the","convolutional","weights",",","re-convert","weights","into","the","equivalent","dense","layer","."]
code: def re_convert(self) -> None:
      """
      After an update on the convolutional weights, re-convert weights into the equivalent dense layer.
      """
      for op in self.ops:
          if isinstance(op, PyTorchIntervalConv2D):
              op.re_convert(self.device)
  
imports: from typing import List, Optional, Tuple, Union, Callable, Any, TYPE_CHECKING
  import logging
  import warnings
  import numpy
  import torch
  from art.estimators.certification.interval.interval import PyTorchIntervalConv2D, PyTorchIntervalDense, PyTorchIntervalReLU, PyTorchIntervalBounds
  from art.estimators.classification.pytorch import PyTorchClassifier
label_desc: Use image node_id 6 for calling the ConvertedModel obj's underlying member method code with example usage: obj.re_convert() without return types