Could someone tell me what this means

[superspaceshuttlee]

Create a linear regression model class

class LinearRegressionModelV1(torch.nn.Module):
    def init(self):
        super().init()
        self.weights = torch.nn.Parameter(torch.randn(1,
                                                      requires_grad=True,
                                                      dtype=torch.float))
        self.bias = torch.nn.Parameter(torch.randn(1,
                                         dtype = torch.float))
        def forward(self, x: torch.Tensor) -> torch.Tensor:
            return self.weights * x + self.bias

I was watching the youtube tutorial and got stuck here
I don't understand what any of this code means but understood everything else before

[LuluW8071]

In simpler way we are creating a class for our model Linear Regression through formula

$$y = m*x + c$$

• __init__ is a constructor of the class where we initialize the parameter: weights ($m$) and bias ($c$)
• forward is a method defines how the model should calculate output ($y$) when given weights ($m$) and bias ($c$) as initialized parameter and $x$ as input data

[superspaceshuttlee]

Thanks