parameters: (self, axioms)
docstring: Compute the embedding for each of the axioms.
  
      When creating a new model, this should be overwritten.
  
      Args:
        axioms: a bool tensor of shape [batch_size, text_length, num_chars]
  
      Returns:
        axiom_embeddings: the tensor representing the embedding of each
          axiom. Recommended to be a float32 tensor of shape
          [batch_size, axiom_embedding_size]
docstring_summary: Compute the embedding for each of the axioms.
docstring_tokens: Compute the embedding for each of the axioms .
function: def axiom_embedding(self, axioms):
      """Compute the embedding for each of the axioms.
  
      When creating a new model, this should be overwritten.
  
      Args:
        axioms: a bool tensor of shape [batch_size, text_length, num_chars]
  
      Returns:
        axiom_embeddings: the tensor representing the embedding of each
          axiom. Recommended to be a float32 tensor of shape
          [batch_size, axiom_embedding_size]
      """
      raise NotImplementedError('Use a derived model')
function_tokens: def axiom_embedding ( self , axioms ) : raise NotImplementedError ( 'Use a derived model' )