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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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):
 """ 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. """

        raise NotImplementedError