The F.linear comes from the nn.Linear from the torch.nn module. Inside the nn.Linear layer the code is as follows (official documentation from https://pytorch.org/docs/master/_modules/torch/nn/modules/linear.html#Linear):
class Linear(Module):

    __constants__ = ['in_features', 'out_features']
    in_features: int
    out_features: int
    weight: Tensor

    def __init__(self, in_features: int, out_features: int, bias: bool = True) -> None:
        super(Linear, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.weight = Parameter(torch.Tensor(out_features, in_features))
        if bias:
            self.bias = Parameter(torch.Tensor(out_features))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self) -> None:
        init.kaiming_uniform_(self.weight, a=math.sqrt(5))
        if self.bias is not None:
            fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
            bound = 1 / math.sqrt(fan_in)
            init.uniform_(self.bias, -bound, bound)

    def forward(self, input: Tensor) -> Tensor:
        return F.linear(input, self.weight, self.bias)

As you can see the line
self.weight = Parameter(torch.Tensor(out_features, in_features)) defines the weight 

defines the weight that is passed to F.linear which performs the output = input.matmul(weight.t()) operation.
Please correct me if I am wrong.