class_docstr: The layer that calculates the tanh activation function
  for each element of the single input:
  :math:`f(x) = (e^{2 * x} - 1) / (e^{2 * x} + 1)`
  
  :param input_layer: The input layer and the number of its output. If no number
      is specified, the first output will be connected.
  :type input_layer: object, tuple(object, int)
  :param name: The layer name.
  :type name: str, default=None
  
  .. rubric:: Layer inputs:
  
  (1) a data blob of any size
  
  .. rubric:: Layer outputs:
  
  (1) a data blob of the same size as the input,
      with activation function values on each of the input elements
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code: def __init__(self, input_layer, name=None):
      if type(input_layer) is PythonWrapper.Tanh:
          super().__init__(input_layer)
          return
  
      layers, outputs = check_input_layers(input_layer, 1)
  
      internal = PythonWrapper.Tanh(
          str(name), layers[0], int(outputs[0])
      )
      super().__init__(internal)
  
imports: import neoml.PythonWrapper
  from .Dnn import Layer
  from neoml.Utils import check_input_layers
  import typing
label_desc: Use image node_id 1 to create a new Tanh object from inherited base classes: Layer with example: obj = Tanh(input_layer, name)