Thanks, @ptrblck  for your reply. I have done this way… But I am not sure about how to use ‘fit’ in this example. can you please help me.
modelA = models.vgg16(pretrained=True)
num_ftrs = modelA.classifier[6].in_features
modelA.classifier[6] = nn.Linear(num_ftrs,2)
modelA.load_state_dict(torch.load('checkpoint1.pt'))
modelB = models.vgg16(pretrained=True)
num_ftrs = modelB.classifier[6].in_features
modelB.classifier[6] = nn.Linear(num_ftrs,2)
modelB.load_state_dict(torch.load('checkpoint2.pt'))
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(1, 100)
        self.fc2 = nn.Linear(100, 100)
        self.fc3 = nn.Linear(100, 2)
        #self.softmax = nn.Softmax(dim=1)

    def forward(self, X):
        X = F.relu(self.fc1(X))
        X = self.fc2(X)
        X = self.fc3(X)
        #X = self.softmax(X)
        return X
modelC = Net() 
modelC.load_state_dict(torch.load('checkpoint_3.pt')) 
or param in modelA.parameters():
    param.requires_grad_(False)
for param in modelB.parameters():
    param.requires_grad_(False)
for param in modelC.parameters():
    param.requires_grad_(False)   
x1 = torch.randn(6, 3, 224, 224)
x2 = torch.randn(6, 3, 224, 224)
x3=torch.randn(6, 1)
y = np.array([1, 1, 1, 2, 2, 2])
eclf1 = VotingClassifier(estimators=[modelA, modelB, modelC], voting='hard')
eclf = eclf1.fit(.....)