def forward(self, # pylint: disable=arguments-differ
                embeddings: torch.FloatTensor,
                mask: torch.LongTensor,
                num_items_to_keep: Union[int, torch.LongTensor]) -> Tuple[torch.FloatTensor, torch.LongTensor,
                                                                          torch.LongTensor, torch.FloatTensor]:
        """
        Extracts the top-k scoring items with respect to the scorer. We additionally return
        the indices of the top-k in their original order, not ordered by score, so that downstream
        components can rely on the original ordering (e.g., for knowing what spans are valid
        antecedents in a coreference resolution model). May use the same k for all sentences in
        minibatch, or different k for each.

        Parameters
        ----------
        embeddings : ``torch.FloatTensor``, required.
            A tensor of shape (batch_size, num_items, embedding_size), containing an embedding for
            each item in the list that we want to prune.
        mask : ``torch.LongTensor``, required.
            A tensor of shape (batch_size, num_items), denoting unpadded elements of
            ``embeddings``.
        num_items_to_keep : ``Union[int, torch.LongTensor]``, required.
            If a tensor of shape (batch_size), specifies the number of items to keep for each
            individual sentence in minibatch.
            If an int, keep the same number of items for all sentences.

        Returns
        -------
        top_embeddings : ``torch.FloatTensor``
            The representations of the top-k scoring items.
            Has shape (batch_size, max_num_items_to_keep, embedding_size).
        top_mask : ``torch.LongTensor``
            The corresponding mask for ``top_embeddings``.
            Has shape (batch_size, max_num_items_to_keep).
        top_indices : ``torch.IntTensor``
            The indices of the top-k scoring items into the original ``embeddings``
            tensor. This is returned because it can be useful to retain pointers to
            the original items, if each item is being scored by multiple distinct
            scorers, for instance. Has shape (batch_size, max_num_items_to_keep).
        top_item_scores : ``torch.FloatTensor``
            The values of the top-k scoring items.
            Has shape (batch_size, max_num_items_to_keep, 1).
        """
        # If an int was given for number of items to keep, construct tensor by repeating the value.
        if isinstance(num_items_to_keep, int):
            batch_size = mask.size(0)
            # Put the tensor on same device as the mask.
            num_items_to_keep = num_items_to_keep * torch.ones([batch_size], dtype=torch.long,
                                                               device=mask.device)

        max_items_to_keep = num_items_to_keep.max()

        mask = mask.unsqueeze(-1)
        num_items = embeddings.size(1)
        # Shape: (batch_size, num_items, 1)
        scores = self._scorer(embeddings)

        if scores.size(-1) != 1 or scores.dim() != 3:
            raise ValueError(f"The scorer passed to Pruner must produce a tensor of shape"
                             f"(batch_size, num_items, 1), but found shape {scores.size()}")
        # Make sure that we don't select any masked items by setting their scores to be very
        # negative.  These are logits, typically, so -1e20 should be plenty negative.
        scores = util.replace_masked_values(scores, mask, -1e20)

        # Shape: (batch_size, max_num_items_to_keep, 1)
        _, top_indices = scores.topk(max_items_to_keep, 1)

        # Mask based on number of items to keep for each sentence.
        # Shape: (batch_size, max_num_items_to_keep)
        top_indices_mask = util.get_mask_from_sequence_lengths(num_items_to_keep, max_items_to_keep)
        top_indices_mask = top_indices_mask.byte()

        # Shape: (batch_size, max_num_items_to_keep)
        top_indices = top_indices.squeeze(-1)

        # Fill all masked indices with largest "top" index for that sentence, so that all masked
        # indices will be sorted to the end.
        # Shape: (batch_size, 1)
        fill_value, _ = top_indices.max(dim=1)
        fill_value = fill_value.unsqueeze(-1)
        # Shape: (batch_size, max_num_items_to_keep)
        top_indices = torch.where(top_indices_mask, top_indices, fill_value)

        # Now we order the selected indices in increasing order with
        # respect to their indices (and hence, with respect to the
        # order they originally appeared in the ``embeddings`` tensor).
        top_indices, _ = torch.sort(top_indices, 1)

        # Shape: (batch_size * max_num_items_to_keep)
        # torch.index_select only accepts 1D indices, but here
        # we need to select items for each element in the batch.
        flat_top_indices = util.flatten_and_batch_shift_indices(top_indices, num_items)

        # Shape: (batch_size, max_num_items_to_keep, embedding_size)
        top_embeddings = util.batched_index_select(embeddings, top_indices, flat_top_indices)

        # Combine the masks on spans that are out-of-bounds, and the mask on spans that are outside
        # the top k for each sentence.
        # Shape: (batch_size, max_num_items_to_keep)
        sequence_mask = util.batched_index_select(mask, top_indices, flat_top_indices)
        sequence_mask = sequence_mask.squeeze(-1).byte()
        top_mask = top_indices_mask & sequence_mask
        top_mask = top_mask.long()

        # Shape: (batch_size, max_num_items_to_keep, 1)
        top_scores = util.batched_index_select(scores, top_indices, flat_top_indices)

        return top_embeddings, top_mask, top_indices, top_scores