I am using pytorch 0.4.
I get the following error when I use capitalized Tensor
>>> torch.Tensor(1)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/lib/python3.6/site-packages/torch/tensor.py", line 57, in __repr__
    return torch._tensor_str._str(self)
  File "/usr/lib/python3.6/site-packages/torch/_tensor_str.py", line 218, in _str
    fmt, scale, sz = _number_format(self)
  File "/usr/lib/python3.6/site-packages/torch/_tensor_str.py", line 96, in _number_format
    if value != math.ceil(value.item()):
RuntimeError: Overflow when unpacking long

but no problem if I use torch.tensor(1).
However, one good thing about capitalized Tensor is that I can specify shape like torch.Tensor(2,3) which gives me
tensor([[-1.0365e+18,  3.0754e-41,  5.5266e+30],
        [ 3.0754e-41,  4.4842e-44,  0.0000e+00]])

I can’t do that with torch.tensor(2,3) which gives me
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: tensor() takes 1 positional argument but 2 were given

Are  all these behaviors expected? When should I use which?