I figured out, how to do this.
just add at initialization of model
self.ups = nn.Upsample(28)

and change forward as
    def forward(self, x):
        x1 = self.midlevel_feature_extractor(x) #midlevel features
        x2 = self.global_feature_extractor(x) #global features
        x2 = x2.view(x2.size()[0], x2.size()[1], 1, 1)
        x2 = self.ups(x2)

        #concat
        concatenated_features = torch.cat((x1, x2), dim=1)
        ...