How to add noise to the intermediate layer of huggingface bert model?

I am trying to add noise to the embeddings from the 5th layer of bert model before it is forwarded to the next layer. The final layer output after the noise is added is then used for classification task. How can I do this in huggingface bert model?

class MainModel(BertPreTrainedModel):
    def __init__(self, config, loss_fn):
        self.num_labels = 2
        self.loss_fn = loss_fn
        config.output_hidden_states = True
        self.bert = AutoModel.from_pretrained("bert-base-uncased",config = config)
        self.classifier = nn.Linear(768, self.num_labels)

    def forward(self, input_ids, attention_mask, token_type_ids, labels, noise, device):
        output = self.bert(input_ids, attention_mask = attention_mask, token_type_ids = token_type_ids)
        hidden_emb = output.hidden_states[5]
        hidden_emb = hidden_emb + noise

        #Forward these hidden_emb to next layer of bert model 
        #Write the code here

        output = output.last_hidden_state #Output from final layer of bert model after adding noise
        output = output[:,0,:]
        classifier_out = self.classifier(output)
        main_prob = F.softmax(classifier_out, dim = 1)
        main_gold_prob = torch.gather(main_prob, 1, labels)
        loss_main = self.loss_fn.forward(main_gold_prob)
        return loss_main,main_prob