class_docstr: The layer that adds the same object to all objects in its first input.
  
  :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 name: The layer name.
  :type name: str, default=None
  
  .. rubric:: Layer inputs:
  
  (1) the data blob of the dimensions:
  
      - **BatchLength** * **BatchWidth** * **ListSize** - the number of objects
      - **Height** * **Width** * **Depth** * **Channels** - object size
  
  (2) the data blob with the object to add to the first input.
      The dimensions:
  
      - **Height**, **Width**, **Depth**, **Channels** are the same as for the first input
      - the other dimensions are 1
  
  .. rubric:: Layer outputs:
  
  (1) a blob that contains the result of adding 
      the second input to each object of the first.
      The dimensions are the same as for the first input.
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code: def __init__(self, input_layers, name=None):
      if type(input_layers) is PythonWrapper.AddToObject:
          super().__init__(input_layers)
          return
  
      layers, outputs = check_input_layers(input_layers, 2)
  
      internal = PythonWrapper.AddToObject(
          str(name),
          layers[0],
          layers[1],
          int(outputs[0]),
          int(outputs[1]),
      )
      super().__init__(internal)
  
imports: import neoml.PythonWrapper
  from .Dnn import Layer
  from neoml.Utils import check_input_layers
label_desc: Use image node_id 1 to create a new AddToObject object from inherited base classes: Layer with example: obj = AddToObject(input_layers, name)