def forward(self, x, cond):
        """Forward pass

        Args:
            x (torch.Tensor - NCHW): Main feature
            cond (torch.Tensor - NCHW): condition feature

        Returns:
            torch.Tensor: Output log binomial distribution
        """
        pt = self.mlp(torch.concat((x, cond), dim=1))
        p, t = pt[:, :2, ...], pt[:, 2:, ...]

        p = p + self.p_eps
        p = p[:, 0, ...] / (p[:, 0, ...] + p[:, 1, ...])

        t = t + self.p_eps
        t = t[:, 0, ...] / (t[:, 0, ...] + t[:, 1, ...])
        t = t.unsqueeze(1)
        t = (self.max_temp - self.min_temp) * t + self.min_temp

        return self.log_binomial_transform(p, t)
Forward pass

        Args:
            x (torch.Tensor - NCHW): Main feature
            cond (torch.Tensor - NCHW): condition feature

        Returns:
            torch.Tensor: Output log binomial distribution
        
ControlNet-v1-1-nightly/annotator/zoe/zoedepth/models/layers/dist_layers.py