class_docstr: Computes span representations by generating an unnormalized attention score for each
  word in the document. Spans representations are computed with respect to these
  scores by normalising the attention scores for words inside the span.
  
  Given these attention distributions over every span, this module weights the
  corresponding vector representations of the words in the span by this distribution,
  returning a weighted representation of each span.
  
  Registered as a `SpanExtractor` with name "self_attentive".
  
  # Parameters
  
  input_dim : `int`, required.
      The final dimension of the `sequence_tensor`.
  num_width_embeddings : `int`, optional (default = `None`).
      Specifies the number of buckets to use when representing
      span width features.
  span_width_embedding_dim : `int`, optional (default = `None`).
      The embedding size for the span_width features.
  bucket_widths : `bool`, optional (default = `False`).
      Whether to bucket the span widths into log-space buckets. If `False`,
      the raw span widths are used.
  
  # Returns
  
  attended_text_embeddings : `torch.FloatTensor`.
      A tensor of shape (batch_size, num_spans, input_dim), which each span representation
      is formed by locally normalising a global attention over the sequence. The only way
      in which the attention distribution differs over different spans is in the set of words
      over which they are normalized.
code: def get_output_dim(self) -> int:
      if self._span_width_embedding is not None:
          return (
              self._input_dim
              + self._span_width_embedding.get_output_dim()
          )
      return self._input_dim
  
file_path: allennlp/allennlp/modules/span_extractors/self_attentive_span_extractor.py
imports: import torch
  from allennlp.modules.span_extractors.span_extractor import SpanExtractor
  from allennlp.modules.span_extractors.span_extractor_with_span_width_embedding import SpanExtractorWithSpanWidthEmbedding
  from allennlp.modules.time_distributed import TimeDistributed
  from allennlp.nn import util
label_desc: Use image node_id 2 for calling the SelfAttentiveSpanExtractor obj's underlying member method code with example usage: obj.get_output_dim() and returns: self, unknown