optimum/onnxruntime/configuration.py [448:492]:
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        is_static: bool,
        use_symmetric_activations: bool = False,
        use_symmetric_weights: bool = True,
        per_channel: bool = True,
        reduce_range: bool = False,
        nodes_to_quantize: Optional[List[str]] = None,
        nodes_to_exclude: Optional[List[str]] = None,
        operators_to_quantize: Optional[List[str]] = None,
    ) -> QuantizationConfig:
        """
        Creates a [`~onnxruntime.QuantizationConfig`] fit for CPU with AVX2 instruction set.

        Args:
            is_static (`bool`):
                Boolean flag to indicate whether we target static or dynamic quantization.
            use_symmetric_activations (`bool`, defaults to `False`):
                Whether to use symmetric quantization for activations.
            use_symmetric_weights (`bool`, defaults to `True`):
                Whether to use symmetric quantization for weights.
            per_channel (`bool`, defaults to `True`):
                Whether we should quantize per-channel (also known as "per-row"). Enabling this can
                increase overall accuracy while making the quantized model heavier.
            reduce_range (`bool`, defaults to `False`):
                Indicate whether to use 8-bits integers (False) or reduce-range 7-bits integers (True).
                As a baseline, it is always recommended testing with full range (reduce_range = False) and then, if
                accuracy drop is significant, to try with reduced range (reduce_range = True).
                Intel's CPUs using AVX512 (non VNNI) can suffer from saturation issue when invoking
                the VPMADDUBSW instruction. To counter this, one should use 7-bits rather than 8-bits integers.
            nodes_to_quantize (`Optional[List[str]]`, defaults to `None`):
                Specific nodes to quantize. If `None`, all nodes being operators from `operators_to_quantize` will be quantized.
            nodes_to_exclude (`Optional[List[str]]`, defaults to `None`):
                Specific nodes to exclude from quantization. The list of nodes in a model can be found loading the ONNX model through onnx.load, or through visual inspection with [netron](https://github.com/lutzroeder/netron).
            operators_to_quantize (`Optional[List[str]]`, defaults to `None`):
                Type of nodes to perform quantization on. By default, all the quantizable operators will be quantized. Quantizable operators can be found at https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/python/tools/quantization/registry.py.
        """
        format, mode, operators_to_quantize = default_quantization_parameters(
            is_static, operators_to_quantize=operators_to_quantize
        )

        return QuantizationConfig(
            is_static=is_static,
            format=format,
            mode=mode,
            activations_dtype=QuantType.QUInt8,
            activations_symmetric=use_symmetric_activations,
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optimum/onnxruntime/configuration.py [504:548]:
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        is_static: bool,
        use_symmetric_activations: bool = False,
        use_symmetric_weights: bool = True,
        per_channel: bool = True,
        reduce_range: bool = False,
        nodes_to_quantize: Optional[List[str]] = None,
        nodes_to_exclude: Optional[List[str]] = None,
        operators_to_quantize: Optional[List[str]] = None,
    ) -> QuantizationConfig:
        """
        Creates a [`~onnxruntime.QuantizationConfig`] fit for CPU with AVX512 instruction set.

        Args:
            is_static (`bool`):
                Boolean flag to indicate whether we target static or dynamic quantization.
            use_symmetric_activations (`bool`, defaults to `False`):
                Whether to use symmetric quantization for activations.
            use_symmetric_weights (`bool`, defaults to `True`):
                Whether to use symmetric quantization for weights.
            per_channel (`bool`, defaults to `True`):
                Whether we should quantize per-channel (also known as "per-row"). Enabling this can
                increase overall accuracy while making the quantized model heavier.
            reduce_range (`bool`, defaults to `False`):
                Indicate whether to use 8-bits integers (False) or reduce-range 7-bits integers (True).
                As a baseline, it is always recommended testing with full range (reduce_range = False) and then, if
                accuracy drop is significant, to try with reduced range (reduce_range = True).
                Intel's CPUs using AVX512 (non VNNI) can suffer from saturation issue when invoking
                the VPMADDUBSW instruction. To counter this, one should use 7-bits rather than 8-bits integers.
            nodes_to_quantize (`Optional[List[str]]`, defaults to `None`):
                Specific nodes to quantize. If `None`, all nodes being operators from `operators_to_quantize` will be quantized.
            nodes_to_exclude (`Optional[List[str]]`, defaults to `None`):
                Specific nodes to exclude from quantization. The list of nodes in a model can be found loading the ONNX model through onnx.load, or through visual inspection with [netron](https://github.com/lutzroeder/netron).
            operators_to_quantize (`Optional[List[str]]`, defaults to `None`):
                Type of nodes to perform quantization on. By default, all the quantizable operators will be quantized. Quantizable operators can be found at https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/python/tools/quantization/registry.py.
        """
        format, mode, operators_to_quantize = default_quantization_parameters(
            is_static, operators_to_quantize=operators_to_quantize
        )

        return QuantizationConfig(
            is_static=is_static,
            format=format,
            mode=mode,
            activations_dtype=QuantType.QUInt8,
            activations_symmetric=use_symmetric_activations,
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