I need a clarification of code written for some code in FastAI2 library.
this is the code WeightDropout written in FastAI2 library.
 class WeightDropout(Module):
        "A module that warps another layer in which some weights will be replaced by 0 during training."

        def __init__(self, module, weight_p, layer_names='weight_hh_l0'):
            self.module,self.weight_p,self.layer_names = module,weight_p,L(layer_names)
            for layer in self.layer_names:
                #Makes a copy of the weights of the selected layers.
                w = getattr(self.module, layer)
                delattr(self.module, layer)
                self.register_parameter(f'{layer}_raw', nn.Parameter(w.data))
                setattr(self.module, layer, F.dropout(w.data, p=self.weight_p, training=False))
                if isinstance(self.module, (nn.RNNBase, nn.modules.rnn.RNNBase)):
                    self.module.flatten_parameters = self._do_nothing

        def _setweights(self):
            "Apply dropout to the raw weights."
            for layer in self.layer_names:
                raw_w = getattr(self, f'{layer}_raw')
                setattr(self.module, layer, F.dropout(raw_w.data, p=self.weight_p, training=self.training))

        def forward(self, *args):
            self._setweights()
            with warnings.catch_warnings():
                #To avoid the warning that comes because the weights aren't flattened.
                warnings.simplefilter("ignore")
                return self.module.forward(*args)

        def reset(self):
            for layer in self.layer_names:
                raw_w = getattr(self, f'{layer}_raw')
                setattr(self.module, layer, 
    F.dropout(raw_w.data, p=self.weight_p, training=False))
            if hasattr(self.module, 'reset'): self.module.reset()

        def _do_nothing(self): pass

where above code randomly drops weights in weight matrix of hidden layers.I am primarily interested in ,
 def _setweights(self):
                "Apply dropout to the raw weights."
                for layer in self.layer_names:
                    raw_w = getattr(self, f'{layer}_raw')
                    setattr(self.module, layer, F.dropout(raw_w.data, p=self.weight_p, training=self.training))

my question is that, does this operation of changing weights is recorded in gradient computation.