def forward()

in grok/transformer.py [0:0]

• 14 lines of code
• 3 McCabe index (conditional complexity)

    def forward(self, input: Tensor) -> Tensor:
        if self.weight_noise > 0 and self.training:
            weight = self.weight + torch.randn_like(self.weight) * self.weight_noise
            # weight = self.weight * torch.exp(torch.randn_like(self.weight) * self.weight_noise)
        else:
            weight = self.weight
        return F.embedding(
            input,
            weight,
            self.padding_idx,
            self.max_norm,
            self.norm_type,
            self.scale_grad_by_freq,
            self.sparse,
        )