Model code:
from transformers import BertTokenizer, BertConfig, BertForMaskedLM, BertForNextSentencePrediction,BertPreTrainedModel
from transformers import BertModel
import torch.nn as nn
import config
import torch


class FCLayer(nn.Module):
    def __init__(self, input_dim, output_dim, dropout_rate=0., use_activation=True):
        super(FCLayer, self).__init__()
        self.use_activation = use_activation
        self.dropout = nn.Dropout(dropout_rate)
        self.linear = nn.Linear(input_dim, output_dim)
        self.tanh = nn.Tanh()

    def forward(self, x):
        x = self.dropout(x)
        if self.use_activation:
            x = self.tanh(x)

        return self.linear(x)

class Mybert(BertPreTrainedModel):
    def __init__(self, bert_config, args):
        super(Mybert, self).__init__(bert_config)
        self.bert = BertModel.from_pretrained(args.pretrained_model_name, config=bert_config)  # Load pretrained bert

        self.num_labels = bert_config.num_labels

        self.cls_fc_layer = FCLayer(bert_config.hidden_size, bert_config.hidden_size, args.dropout_rate)
        self.e1_fc_layer = FCLayer(bert_config.hidden_size, bert_config.hidden_size, args.dropout_rate)
        self.e2_fc_layer = FCLayer(bert_config.hidden_size, bert_config.hidden_size, args.dropout_rate)
        self.label_classifier = FCLayer(bert_config.hidden_size * 3, bert_config.num_labels, args.dropout_rate, use_activation=False)

    @staticmethod
    def entity_average(hidden_output, e_mask):
        """
        Average the entity hidden state vectors (H_i ~ H_j)
        :param hidden_output: [batch_size, j-i+1, dim]
        :param e_mask: [batch_size, max_seq_len]
                e.g. e_mask[0] == [0, 0, 0, 1, 1, 1, 0, 0, ... 0]
        :return: [batch_size, dim]
        """
        e_mask_unsqueeze = e_mask.unsqueeze(1)  # [b, 1, j-i+1]
        length_tensor = (e_mask != 0).sum(dim=1).unsqueeze(1)  # [batch_size, 1]

        sum_vector = torch.bmm(e_mask_unsqueeze.float(), hidden_output).squeeze(1)  # [b, 1, j-i+1] * [b, j-i+1, dim] = [b, 1, dim] -> [b, dim]
        avg_vector = sum_vector.float() / length_tensor.float()  # broadcasting
        return avg_vector

    def forward(self, input_ids, attention_mask, token_type_ids, labels, e1_mask, e2_mask):
        outputs = self.bert(input_ids, attention_mask=attention_mask,
                            token_type_ids=token_type_ids)  # sequence_output, pooled_output, (hidden_states), (attentions)
        sequence_output = outputs[0]
        pooled_output = outputs[1]  # [CLS]

        # Average
        e1_h = self.entity_average(sequence_output, e1_mask)
        e2_h = self.entity_average(sequence_output, e2_mask)

        # Dropout -> tanh -> fc_layer
        pooled_output = self.cls_fc_layer(pooled_output)
        e1_h = self.e1_fc_layer(e1_h)
        e2_h = self.e2_fc_layer(e2_h)

        # Concat -> fc_layer
        concat_h = torch.cat([pooled_output, e1_h, e2_h], dim=-1)
        logits = self.label_classifier(concat_h)

        return logits