torch.Tensor() new() received an invalid combination of arguments – got (list, dtype=torch.dtype)

Capitalization matters — in your top example, you have Tensor with uppercase T, but the documentation excerpt is talking about tensor with lowercase t.

As Jan’s answer said but here I will explain why
Tensor different from tensor

torch.Tensor is an alias for the default tensor type (torch.FloatTensor).
which is a CPU tensor, 32-bit floating-point so the type and device are predetermined.

#This will cause an Error, because Tensor has no dtype and device properties on it’s constructor

example_tensor = torch.Tensor(
    [
     [[1, 2], [3, 4]], 
     [[5, 6], [7, 8]], 
     [[9, 0], [1, 2]]
    ]
, dtype=torch.float32, device=device)

To move a tensor to a new device, you can write new_tensor = example_tensor.to(device) where the device will be either CPU or Cuda.
and to set new type you can use new_tensor.dtype=torch.float64 for example

     example_tensor = torch.Tensor(
    [
     [[1, 2], [3, 4]], 
     [[5, 6], [7, 8]], 
     [[9, 0], [1, 2]]
    ]
)
    new_tensor = example_tensor.to(device)
    new_tensor.dtype  #set or get dtype

this is from the documentation

A tensor (case senstive tensor not Tensor) of specific data type can be constructed by passing a torch.dtype and/or a torch.device to a constructor or tensor creation op:

cuda0 = torch.device('cuda:0')
torch.ones([2, 4], dtype=torch.float64, device=cuda0)

.