self.parameters()  is a generator method that iterates over the parameters of the model.
So  weight variable simply holds a parameter of the model.
Then weight.new() creates a tensor that has the same data type, same device as the produced parameter.
class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc = nn.Linear(10,1)
        
model = Model()
weight = next(model.parameters()).data

print(weight)
# Output :  tensor([[ 0.0398,  0.0729, -0.2676,  0.2354, -0.0853,  0.1141,  0.0297,  0.0257, \ 
         -0.1303,  0.2208]])

print(type(weight), weight.device, weight.dtype, weight.requires_grad)
# Output : (torch.Tensor, device(type='cpu'), torch.float32, False)

var = weight.new(4,5)

print(type(var), var.device, var.dtype,  var.requires_grad)
# Output : (torch.Tensor, device(type='cpu'), torch.float32, False)