parameters: (self)
return_statement: return super(Value, self).mask().asarray()
docstring: The mask matrix of this value. Each row denotes a sequence with its
          elements describing the mask of the element:
  
           * 2: beginning of sequence (e.g. an LSTM would be reset)
           * 1: valid element
           * 0: invalid element
  
          Example:
            A mask of ``[[2, 1, 1], [1, 1, 0]]`` describes a batch of two
            sequences. The first has three elements, of which the first element
            (2) signals the beginning of a sequence. The second sequence has two
            elements (last element marked 'invalid' by '0'). As it starts with
            (1), it is a continuation of the 2nd sequence in the previous
            minibatch.
docstring_summary: The mask matrix of this value. Each row denotes a sequence with its
          elements describing the mask of the element:
docstring_tokens: The mask matrix of this value . Each row denotes a sequence with its elements describing the mask of the element :
function: def mask(self):
          '''
          The mask matrix of this value. Each row denotes a sequence with its
          elements describing the mask of the element:
  
           * 2: beginning of sequence (e.g. an LSTM would be reset)
           * 1: valid element
           * 0: invalid element
  
          Example:
            A mask of ``[[2, 1, 1], [1, 1, 0]]`` describes a batch of two
            sequences. The first has three elements, of which the first element
            (2) signals the beginning of a sequence. The second sequence has two
            elements (last element marked 'invalid' by '0'). As it starts with
            (1), it is a continuation of the 2nd sequence in the previous
            minibatch.
          '''
          return super(Value, self).mask().asarray()
function_tokens: def mask ( self ) : return super ( Value , self ) . mask ( ) . asarray ( )