class_docstr: The layer that transforms a set of two-dimensional multi-channel images
  into a set of images of smaller size but with more channels.
  This operation is used in YOLO architecture: https://pjreddie.com/darknet/yolo/
  
  :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 stride: The value by which the image size will be divided in the final result.
  :type stride: int, default=1
  :param name: The layer name.
  :type name: str, default=None
  
  .. rubric:: Layer inputs:
  
  (1) a blob with images.
      The dimensions:
  
      - **BatchLength** * **BatchWidth** * **ListSize** is the number of images
      - **Height** is the image height; should be a multiple of stride
      - **Width** is the image width; should be a multiple of stride
      - **Depth** is 1
      - **Channels** is the number of channels the image format uses
  
  .. rubric:: Layer outputs:
  
  (1) the result of image transformation.
      The dimensions:
  
      - **BatchLength**, **BatchWidth**, **ListSize** are the same as for the input
      - **Height** is input Height / stride
      - **Width** is input **Width** / stride
      - **Depth** is 1
      - **Channels** is input **Channels** * stride^2
docstr: Sets the divider value.
          
docstr_tok: ["Sets","the","divider","value","."]
code: def stride(self, stride):
      """Sets the divider value."""
      if stride < 1:
          raise ValueError("The `stride` must be > 0.")
  
      self._internal.set_stride(int(stride))
  
file_path: neoml/NeoML/Python/neoml/Dnn/Reorg.py
imports: import neoml.PythonWrapper
  from .Dnn import Layer
  from neoml.Utils import check_input_layers
label_desc: Use image node_id 3 for calling the Reorg obj's underlying member method code with example usage: obj.stride(stride) without return types