class_docstr: The layer that converts the input sequence 
  into the output sequence, not necessarily of the same length
  
  :param input_layers: The input layers to be connected. 
      The integer in each tuple specifies the number of the output.
      If not set, the first output will be used.
  :type input_layers: list of object, tuple(object, int)
  :param score: The type of estimate function used to check alignment 
      of input and output sequences:
  
      - ``additive`` is tanh(x*Wx + y*Wy)*v
      - ``dot_product`` is x*W*y
  :type score: str, {'additive', 'dot_product'}
  :param hidden_size: The size of the hidden layer.
  :type hidden_size: int
  :param output_object_size: The number of channels in the output object.
  :type output_object_size: int
  :param output_seq_len: The length of the output sequence.
  :type output_seq_len: int
  :param name: The layer name.
  :type name: str, default=None
  
  .. rubric:: Layer inputs:
  
  (1) a data blob of any size with the input sequence
  
  (2) the special character used to initialize the output sequence
      All dimensions are equal to 1
  
  .. rubric:: Layer outputs:
  
  (1) the output sequence
      The dimensions:
  
      - **BatchLength** equal to output_seq_len
      - **Channels** equal to output_object_size
      - all other dimensions are equal to 1
docstr: Gets the length of the output sequence.
          
docstr_tok: ["Gets","the","length","of","the","output","sequence","."]
code: def output_seq_len(self):
      """Gets the length of the output sequence."""
      return self._internal.get_output_seq_len()
  
file_path: neoml/NeoML/Python/neoml/Dnn/AttentionDecoder.py
imports: import neoml.PythonWrapper
  from .Dnn import Layer
  from neoml.Utils import check_input_layers
label_desc: Use image node_id 4 for calling the AttentionDecoder obj's underlying member method code with example usage: obj.output_seq_len() and returns: self