code: def forward(self, # pylint: disable=arguments-differ
                  sequence_tensor: torch.FloatTensor,
                  span_indices: torch.LongTensor,
                  sequence_mask: torch.LongTensor = None,
                  span_indices_mask: torch.LongTensor = None):
          
          raise NotImplementedError
docs: 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.LongTensor``, optional (default = ``None``).
              A tensor of shape (batch_size, sequence_length) representing padded
              elements of the sequence.
          span_indices_mask : ``torch.LongTensor``, 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.