def forward(self, hidden, labels=None, keep_order=False):
        """
        Params:
            hidden :: [len*bsz x d_proj]
            labels :: [len*bsz]

        Return:
            if labels is None: out :: [len*bsz x n_tokens] log probabilities of tokens over the vocabulary else: out ::
            [(len-1)*bsz] Negative log likelihood. We could replace this implementation by the native PyTorch one if
            theirs had an option to set bias on all clusters in the native one. here:
            https://github.com/pytorch/pytorch/blob/dbe6a7a9ff1a364a8706bf5df58a1ca96d2fd9da/torch/nn/modules/adaptive.py#L138
        """

        if labels is not None:
            # Shift so that tokens < n predict n
            hidden = hidden[..., :-1, :].contiguous()
            labels = labels[..., 1:].contiguous()
            hidden = hidden.view(-1, hidden.size(-1))
            labels = labels.view(-1)
            if hidden.size(0) != labels.size(0):
                raise RuntimeError("Input and labels should have the same size in the batch dimension.")
        else:
            hidden = hidden.view(-1, hidden.size(-1))

        if self.n_clusters == 0:
            logit = self._compute_logit(hidden, self.out_layers[0].weight, self.out_layers[0].bias, self.out_projs[0])
            if labels is not None:
                mask = labels != -100
                out = torch.zeros_like(labels, dtype=hidden.dtype, device=hidden.device)
                out[mask] = (
                    -nn.functional.log_softmax(logit, dim=-1)[mask].gather(1, labels[mask].unsqueeze(1)).squeeze(1)
                )
            else:
                out = nn.functional.log_softmax(logit, dim=-1)
        else:
            # construct weights and biases
            weights, biases = [], []
            for i in range(len(self.cutoffs)):
                if self.div_val == 1:
                    l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]
                    weight_i = self.out_layers[0].weight[l_idx:r_idx]
                    bias_i = self.out_layers[0].bias[l_idx:r_idx]
                else:
                    weight_i = self.out_layers[i].weight
                    bias_i = self.out_layers[i].bias

                if i == 0:
                    weight_i = torch.cat([weight_i, self.cluster_weight], dim=0)
                    bias_i = torch.cat([bias_i, self.cluster_bias], dim=0)

                weights.append(weight_i)
                biases.append(bias_i)

            head_weight, head_bias, head_proj = weights[0], biases[0], self.out_projs[0]

            head_logit = self._compute_logit(hidden, head_weight, head_bias, head_proj)
            head_logprob = nn.functional.log_softmax(head_logit, dim=1)

            if labels is None:
                out = hidden.new_empty((head_logit.size(0), self.n_token))
            else:
                out = torch.zeros_like(labels, dtype=hidden.dtype, device=hidden.device)

            offset = 0
            cutoff_values = [0] + self.cutoffs
            for i in range(len(cutoff_values) - 1):
                l_idx, r_idx = cutoff_values[i], cutoff_values[i + 1]

                if labels is not None:
                    mask_i = (labels >= l_idx) & (labels < r_idx)
                    indices_i = mask_i.nonzero().squeeze()

                    if indices_i.numel() == 0:
                        continue

                    target_i = labels.index_select(0, indices_i) - l_idx
                    head_logprob_i = head_logprob.index_select(0, indices_i)
                    hidden_i = hidden.index_select(0, indices_i)
                else:
                    hidden_i = hidden

                if i == 0:
                    if labels is not None:
                        logprob_i = head_logprob_i.gather(1, target_i[:, None]).squeeze(1)
                    else:
                        out[:, : self.cutoffs[0]] = head_logprob[:, : self.cutoffs[0]]
                else:
                    weight_i, bias_i, proj_i = weights[i], biases[i], self.out_projs[i]

                    tail_logit_i = self._compute_logit(hidden_i, weight_i, bias_i, proj_i)
                    tail_logprob_i = nn.functional.log_softmax(tail_logit_i, dim=1)
                    cluster_prob_idx = self.cutoffs[0] + i - 1  # No probability for the head cluster
                    if labels is not None:
                        logprob_i = head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
                            1, target_i[:, None]
                        ).squeeze(1)
                    else:
                        logprob_i = head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
                        out[:, l_idx:r_idx] = logprob_i

                if labels is not None:
                    if (hasattr(self, "keep_order") and self.keep_order) or keep_order:
                        out.index_copy_(0, indices_i, -logprob_i)
                    else:
                        out[offset : offset + logprob_i.size(0)].copy_(-logprob_i)
                    offset += logprob_i.size(0)

        return out

        Params:
            hidden :: [len*bsz x d_proj]
            labels :: [len*bsz]

        Return:
            if labels is None: out :: [len*bsz x n_tokens] log probabilities of tokens over the vocabulary else: out ::
            [(len-1)*bsz] Negative log likelihood. We could replace this implementation by the native PyTorch one if
            theirs had an option to set bias on all clusters in the native one. here:
            https://github.com/pytorch/pytorch/blob/dbe6a7a9ff1a364a8706bf5df58a1ca96d2fd9da/torch/nn/modules/adaptive.py#L138
        
dpkd/transformers/src/transformers/models/transfo_xl/modeling_transfo_xl_utilities.py