What does -> torch.Tensor mean?

[mostfa00]

In the code:

class LinearRegressionModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.weights = nn.Paramter(torch.randn(1,
                                               requires_grad=True,
                                               dtype =torch.float))
        
        self.bias = nn.Paramter(torch.randn(1,
                                            requires_grad=True,
                                            dtype =torch.float))
        
        def forward(self, x: torch.Tensor) -> torch.Tensor:
            return self.weights * x + self.bias

What is the meaning of x: torch.Tensor) -> torch.Tensor in last line?

[fivefishstudios]

They're called function typehints. i.e. the output/return value of that function forward() is a type of 'torch.Tensor'

I think it's only used for documentation and code hints and not actually enforced by Python.

[mrdbourke]

Thank you @fivefishstudios for the answer!

And @mostfa00, they aren't required but they do help with code/communication.

Type hints are a way of specifying the expected data types of function arguments and return values in Python.

Here's an example:

from typing import List

def combine_strings(strings: List[str]) -> str:
    return ''.join(strings)

In this example, the combine_strings() function takes a list of strings as an argument and returns a single string that is the concatenation of all the strings in the list.

The List[str] and str type hints specify that the function takes a list of strings as its argument and returns a string (-> str is like saying "returns a string").