import torch
x=torch.arange(10)
y=x.view(-1)
z=x.reshape(2,5)
c=x.clone()
d=x[1::2] 
print(x, x.data_ptr())
print(y, y.data_ptr())
print(z, z.data_ptr())
print(c, c.data_ptr())
print(d, d.data_ptr())
print("...")

print(id(x), id(x.data))
print(id(y), id(y.data))
print(id(z), id(z.data))
print(id(c), id(c.data))
print(id(d), id(d.data))

Returns:
tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) 73968896
tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) 73968896
tensor([[0, 1, 2, 3, 4],
        [5, 6, 7, 8, 9]]) 73968896
tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) 73969024
tensor([1, 3, 5, 7, 9]) 73968904
...
139879908757576 139879908721648
139879908724240 139879908721648
139879908723736 139879908721648
139879908722440 139879908721648
139879908721936 139879909096904

data_ptr() was something I was looking for. For any variable var, id(var) returns different result, and id(var.data) returns the same location id almost always, but not always.