parameters: (
      data: NodeInput,
      input_low: NodeInput,
      input_high: NodeInput,
      output_low: NodeInput,
      output_high: NodeInput,
      levels: int,
      auto_broadcast: str = "NUMPY",
      name: Optional[str] = None,
  )
return_statement: return _get_node_factory_opset1().create(
          "FakeQuantize",
          as_nodes(data, input_low, input_high, output_low, output_high),
          {"levels": levels, "auto_broadcast": auto_broadcast.upper()},
      )
docstring: r"""Perform an element-wise linear quantization on input data.
  
      :param data:           The node with data tensor.
      :param input_low:      The node with the minimum for input values.
      :param input_high:     The node with the maximum for input values.
      :param output_low:     The node with the minimum quantized value.
      :param output_high:    The node with the maximum quantized value.
      :param levels:         The number of quantization levels. Integer value.
      :param auto_broadcast: The type of broadcasting specifies rules used for
                             auto-broadcasting of input tensors.
      :return: New node with quantized value.
  
      Input floating point values are quantized into a discrete set of floating point values.
  
      .. code-block:: python
  
          if x <= input_low:
              output = output_low
          if x > input_high:
              output = output_high
          else:
              output = fake_quantize(output)
  
      Fake quantize uses the following logic:
  
      .. math::
  
          output =
          \dfrac{round( \dfrac{data - input\_low}{(input\_high - input\_low)\cdot (levels-1)})}
          {(levels-1)\cdot (output\_high - output\_low)} + output\_low
docstring_summary: r"""Perform an element-wise linear quantization on input data.
docstring_tokens: r Perform an element - wise linear quantization on input data .
function: def fake_quantize(
      data: NodeInput,
      input_low: NodeInput,
      input_high: NodeInput,
      output_low: NodeInput,
      output_high: NodeInput,
      levels: int,
      auto_broadcast: str = "NUMPY",
      name: Optional[str] = None,
  ) -> Node:
      r"""Perform an element-wise linear quantization on input data.
  
      :param data:           The node with data tensor.
      :param input_low:      The node with the minimum for input values.
      :param input_high:     The node with the maximum for input values.
      :param output_low:     The node with the minimum quantized value.
      :param output_high:    The node with the maximum quantized value.
      :param levels:         The number of quantization levels. Integer value.
      :param auto_broadcast: The type of broadcasting specifies rules used for
                             auto-broadcasting of input tensors.
      :return: New node with quantized value.
  
      Input floating point values are quantized into a discrete set of floating point values.
  
      .. code-block:: python
  
          if x <= input_low:
              output = output_low
          if x > input_high:
              output = output_high
          else:
              output = fake_quantize(output)
  
      Fake quantize uses the following logic:
  
      .. math::
  
          output =
          \dfrac{round( \dfrac{data - input\_low}{(input\_high - input\_low)\cdot (levels-1)})}
          {(levels-1)\cdot (output\_high - output\_low)} + output\_low
      """
      return _get_node_factory_opset1().create(
          "FakeQuantize",
          as_nodes(data, input_low, input_high, output_low, output_high),
          {"levels": levels, "auto_broadcast": auto_broadcast.upper()},
      )
function_tokens: def fake_quantize ( data : NodeInput , input_low : NodeInput , input_high : NodeInput , output_low : NodeInput , output_high : NodeInput , levels : int , auto_broadcast : str = "NUMPY" , name : Optional [ str ] = None , ) -> Node : return _get_node_factory_opset1 ( ) . create ( "FakeQuantize" , as_nodes ( data , input_low , input_high , output_low , output_high ) , { "levels" : levels , "auto_broadcast" : auto_broadcast . upper ( ) } , )