The number of input features in your linear layers should match the number of features from your inputs.
This code should work:
class EnsembleModel(nn.Module):
    
    def __init__(self,out_size,training=True):
        super().__init__()
        self.fc1 = nn.Linear(8192,512)
        self.fc2 = nn.Linear(73728,512)
        self.fc3 = nn.Linear(65536,512)
        self.fc4 = nn.Linear(512,out_size)

    def forward(self,inp1,inp2,inp3):        
        out1 = self.fc1(F.dropout(inp1,training=self.training))
        out2 = self.fc2(F.dropout(inp2,training=self.training))
        out3 = self.fc3(F.dropout(inp3,training=self.training))
        out = out1 + out2 + out3
        out = self.fc4(F.dropout(out,training=self.training))
        return out


inpt1 = torch.randn([64, 8192])
inpt2 = torch.randn([64, 73728])
inpt3 = torch.randn([64, 65536])

model = EnsembleModel(2)
out = model(inpt1, inpt2, inpt3)