    def forward(
        self,
        sequence_tensor: torch.FloatTensor,
        span_indices: torch.LongTensor,
        sequence_mask: torch.BoolTensor = None,
        span_indices_mask: torch.BoolTensor = None,
    ):
        """
        Given a sequence tensor, extract spans and return representations of
        them. Span representation can be computed in many different ways,
        such as concatenation of the start and end spans, attention over the
        vectors contained inside the span, etc.

        # Parameters

        sequence_tensor : `torch.FloatTensor`, required.
            A tensor of shape (batch_size, sequence_length, embedding_size)
            representing an embedded sequence of words.
        span_indices : `torch.LongTensor`, required.
            A tensor of shape `(batch_size, num_spans, 2)`, where the last
            dimension represents the inclusive start and end indices of the
            span to be extracted from the `sequence_tensor`.
        sequence_mask : `torch.BoolTensor`, optional (default = `None`).
            A tensor of shape (batch_size, sequence_length) representing padded
            elements of the sequence.
        span_indices_mask : `torch.BoolTensor`, optional (default = `None`).
            A tensor of shape (batch_size, num_spans) representing the valid
            spans in the `indices` tensor. This mask is optional because
            sometimes it's easier to worry about masking after calling this
            function, rather than passing a mask directly.

        # Returns

        A tensor of shape `(batch_size, num_spans, embedded_span_size)`,
        where `embedded_span_size` depends on the way spans are represented.
        """
        raise NotImplementedError
Given a sequence tensor, extract spans and return representations of
them. Span representation can be computed in many different ways,
such as concatenation of the start and end spans, attention over the
vectors contained inside the span, etc.

# Parameters

sequence_tensor : `torch.FloatTensor`, required.
A tensor of shape (batch_size, sequence_length, embedding_size)
representing an embedded sequence of words.
span_indices : `torch.LongTensor`, required.
A tensor of shape `(batch_size, num_spans, 2)`, where the last
dimension represents the inclusive start and end indices of the
span to be extracted from the `sequence_tensor`.
sequence_mask : `torch.BoolTensor`, optional (default = `None`).
A tensor of shape (batch_size, sequence_length) representing padded
elements of the sequence.
span_indices_mask : `torch.BoolTensor`, optional (default = `None`).
A tensor of shape (batch_size, num_spans) representing the valid
spans in the `indices` tensor. This mask is optional because
sometimes it's easier to worry about masking after calling this
function, rather than passing a mask directly.

# Returns

A tensor of shape `(batch_size, num_spans, embedded_span_size)`,
where `embedded_span_size` depends on the way spans are represented.
allennlp/modules/span_extractors/span_extractor.py