class_docstr: The layer that finds the average over the whole three-dimensional image.
  It functions exactly as MeanPooling3d with the filter size 
  equal to the input image size.
  
  :param input_layers: The input layer and the number of its output. If no number
      is specified, the first output will be connected.
  :type input_layers: object, tuple(object, int)
  :param name: The layer name.
  :type name: str, default=None
  
  .. rubric:: Layer inputs:
  
  (1) the set of images, of dimensions:
  
      - **BatchLength** * **BatchWidth** * **ListSize** - the number of images in the set
      - **Height** - the images' height
      - **Width** - the images' width
      - **Depth** - the images' depth
      - **Channels** - the number of channels the image format uses
      
  .. rubric:: Layer outputs:
  
  (1) the average values over each image.
      The dimensions:
  
      - **BatchLength**, **BatchWidth**, **ListSize** are equal to the input dimensions
      - **Height**, **Width**, **Depth** are 1
      - **Channels** is equal to the input **Channels**
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code: def __init__(self, input_layers, name=None):
      if type(input_layers) is PythonWrapper.GlobalMeanPooling:
          super().__init__(input_layers)
          return
  
      layers, outputs = check_input_layers(input_layers, 1)
  
      internal = PythonWrapper.GlobalMeanPooling(
          str(name), layers[0], outputs[0]
      )
      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 GlobalMeanPooling object from inherited base classes: Layer with example: obj = GlobalMeanPooling(input_layers, name)