parameters: (
      output_size: Node, image_size: NodeInput, attrs: dict, name: Optional[str] = None
  )
return_statement: return _get_node_factory_opset1().create(
          "PriorBoxClustered", [output_size, as_node(image_size)], attrs
      )
docstring: Generate prior boxes of specified sizes normalized to the input image size.
  
      :param  output_size:    1D tensor with two integer elements [height, width]. Specifies the
                              spatial size of generated grid with boxes.
      :param  image_size:     1D tensor with two integer elements [image_height, image_width] that
                              specifies shape of the image for which boxes are generated.
      :param  attrs:          The dictionary containing key, value pairs for attributes.
      :param  name:           Optional name for the output node.
      :return: Node representing PriorBoxClustered operation.
  
       Available attributes are:
  
      * widths        Specifies desired boxes widths in pixels.
                      Range of values: floating point positive numbers.
                      Default value: 1.0
                      Required: no
  
      * heights       Specifies desired boxes heights in pixels.
                      Range of values: floating point positive numbers.
                      Default value: 1.0
                      Required: no
  
      * clip          The flag that denotes if each value in the output tensor should be clipped
                      within [0,1].
                      Range of values: {True, False}
                      Default value: True
                      Required: no
  
      * step_widths   The distance between box centers.
                      Range of values: floating point positive number
                      Default value: 0.0
                      Required: no
  
      * step_heights  The distance between box centers.
                      Range of values: floating point positive number
                      Default value: 0.0
                      Required: no
  
      * offset        The shift of box respectively to the top left corner.
                      Range of values: floating point positive number
                      Default value: None
                      Required: yes
  
      * variance      Denotes a variance of adjusting bounding boxes.
                      Range of values: floating point positive numbers
                      Default value: []
                      Required: no
  
      Example of attribute dictionary:
  
      .. code-block:: python
  
          # just required ones
          attrs = {
              'offset': 85,
          }
  
          attrs = {
              'offset': 85,
              'clip': False,
              'step_widths': [1.5, 2.0, 2.5]
          }
  
      Optional attributes which are absent from dictionary will be set with corresponding default.
docstring_summary: Generate prior boxes of specified sizes normalized to the input image size.
docstring_tokens: Generate prior boxes of specified sizes normalized to the input image size .
function: def prior_box_clustered(
      output_size: Node, image_size: NodeInput, attrs: dict, name: Optional[str] = None
  ) -> Node:
      """Generate prior boxes of specified sizes normalized to the input image size.
  
      :param  output_size:    1D tensor with two integer elements [height, width]. Specifies the
                              spatial size of generated grid with boxes.
      :param  image_size:     1D tensor with two integer elements [image_height, image_width] that
                              specifies shape of the image for which boxes are generated.
      :param  attrs:          The dictionary containing key, value pairs for attributes.
      :param  name:           Optional name for the output node.
      :return: Node representing PriorBoxClustered operation.
  
       Available attributes are:
  
      * widths        Specifies desired boxes widths in pixels.
                      Range of values: floating point positive numbers.
                      Default value: 1.0
                      Required: no
  
      * heights       Specifies desired boxes heights in pixels.
                      Range of values: floating point positive numbers.
                      Default value: 1.0
                      Required: no
  
      * clip          The flag that denotes if each value in the output tensor should be clipped
                      within [0,1].
                      Range of values: {True, False}
                      Default value: True
                      Required: no
  
      * step_widths   The distance between box centers.
                      Range of values: floating point positive number
                      Default value: 0.0
                      Required: no
  
      * step_heights  The distance between box centers.
                      Range of values: floating point positive number
                      Default value: 0.0
                      Required: no
  
      * offset        The shift of box respectively to the top left corner.
                      Range of values: floating point positive number
                      Default value: None
                      Required: yes
  
      * variance      Denotes a variance of adjusting bounding boxes.
                      Range of values: floating point positive numbers
                      Default value: []
                      Required: no
  
      Example of attribute dictionary:
  
      .. code-block:: python
  
          # just required ones
          attrs = {
              'offset': 85,
          }
  
          attrs = {
              'offset': 85,
              'clip': False,
              'step_widths': [1.5, 2.0, 2.5]
          }
  
      Optional attributes which are absent from dictionary will be set with corresponding default.
      """
      requirements = [
          ("widths", False, np.floating, is_positive_value),
          ("heights", False, np.floating, is_positive_value),
          ("clip", False, np.bool_, None),
          ("step_widths", False, np.floating, is_positive_value),
          ("step_heights", False, np.floating, is_positive_value),
          ("offset", True, np.floating, is_positive_value),
          ("variance", False, np.floating, is_positive_value),
      ]
  
      check_valid_attributes("PriorBoxClustered", attrs, requirements)
  
      return _get_node_factory_opset1().create(
          "PriorBoxClustered", [output_size, as_node(image_size)], attrs
      )
function_tokens: def prior_box_clustered ( output_size : Node , image_size : NodeInput , attrs : dict , name : Optional [ str ] = None ) -> Node : requirements = [ ( "widths" , False , np . floating , is_positive_value ) , ( "heights" , False , np . floating , is_positive_value ) , ( "clip" , False , np . bool_ , None ) , ( "step_widths" , False , np . floating , is_positive_value ) , ( "step_heights" , False , np . floating , is_positive_value ) , ( "offset" , True , np . floating , is_positive_value ) , ( "variance" , False , np . floating , is_positive_value ) , ] check_valid_attributes ( "PriorBoxClustered" , attrs , requirements ) return _get_node_factory_opset1 ( ) . create ( "PriorBoxClustered" , [ output_size , as_node ( image_size ) ] , attrs )