class_docstr: The layer that finds the maximum element along the given dimension. 
  
  :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 dimension: The dimension along which the maximum is to be found.
  :type dimension: str, {'batch_length', 'batch_width', 'list_size', 'height', 'width',
      'depth', 'channels'}, default='channels'
  :param name: The layer name.
  :type name: str, default=None
  
  .. rubric:: Layer inputs:
  
  (1) a data blob of any size
  
  .. rubric:: Layer outputs:
  
  (1) contains the coordinate of the maximum
      The dimensions:
  
      - 'dimension' along which the maximum is found is equal to 1
      - all other dimensions are equal to the first input dimensions
code: def __init__(self, input_layer, dimension="channels", name=None):
      if type(input_layer) is PythonWrapper.Argmax:
          super().__init__(input_layer)
          return
  
      layers, outputs = check_input_layers(input_layer, 1)
  
      dimension_index = self.dimensions.index(dimension)
  
      internal = PythonWrapper.Argmax(
          str(name), layers[0], int(outputs[0]), dimension_index
      )
      super().__init__(internal)
  
file_path: neoml/NeoML/Python/neoml/Dnn/Argmax.py
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 Argmax object from inherited base classes: Layer with example: obj = Argmax(input_layer, dimension, name)