src/transformers/models/informer/modeling_informer.py/InformerFeatureEmbedder/forward
class InformerFeatureEmbedder:    def forward(self, features: torch.Tensor) -> torch.Tensor:
        if self.num_features > 1:
            # we slice the last dimension, giving an array of length
            # self.num_features with shape (N,T) or (N)
            cat_feature_slices = torch.chunk(features, self.num_features, dim=-1)
        else:
            cat_feature_slices = [features]

        return torch.cat(
            [
                embed(cat_feature_slice.squeeze(-1))
                for embed, cat_feature_slice in zip(self.embedders, cat_feature_slices)
            ],
            dim=-1,
        )