// storage type. The available values use the full range of the storage value,
// i.e. [-128, 127]. Assumes asymmetric quantization, meaning the zero point
// value can be a non-zero value.
// If `narrow_range` is set true (ex: for weights), a restricted range of
// integers will be used for symmetric mapping, i.e. [-127, 127].
UniformQuantizedType CreateI8F32UniformQuantizedType(Location loc,

    there's any, and set it to True. The reason behind this decision is that
    generally activations of these ops show better accuracy with asymmetric
    input quantization so we want to deprecate symmetric activation quantization
    for those ops eventually.
    - Unlike to the old quantizer, per-channel quantization is supported for
    weight-only TransposeConvOp.
  }];

union QuantizationDetails {
  CustomQuantization,
}

// Parameters for converting a quantized tensor back to float.
table QuantizationParameters {
  // These four parameters are the asymmetric linear quantization parameters.
  // Given a quantized value q, the corresponding float value f should be:
  //   f = scale * (q - zero_point)
  // For other quantization types, the QuantizationDetails below is used.

union QuantizationDetails {
  CustomQuantization,
}

// Parameters for converting a quantized tensor back to float.
table QuantizationParameters {
  // These four parameters are the asymmetric linear quantization parameters.
  // Given a quantized value q, the corresponding float value f should be:
  //   f = scale * (q - zero_point)
  // For other quantization types, the QuantizationDetails below is used.

  } else if (width == 8) {
    // This can be a state tensor, or an actual constant tensor with
    // asymmetric range. For a state tensor, assigning correct quantization
    // parameters is sufficient, and for constants with asymmetric range it's
    // not correctly quantized by legacy quantizer so call the new Quantize.
    return Quantize(real_value, tensor_type);
  } else if (width == 16) {

        return true
    }

    private fun modifiersMatch(psi: KtCallableDeclaration, fir: FirCallableDeclaration): Boolean {
        // According to asymmetric logic in `PsiRawFirBuilder`.
        if (psi.parentsOfType<KtDeclaration>().any { it.hasExpectModifier() } != fir.symbol.rawStatus.isExpect) return false

  func::CallOp call_op_;
};

// Matches the pattern for quantized convolution op and rewrites it to use
// uniform quantized types.
//
// Currently assumes asymmetric per-tensor quantization for activations and
// symmetric per-channel quantization for filters.
//
// This pattern represents the following derived equation, where:
// * rn = real (expressed) value for tensor n
// * qn = quantized value for tensor n

      } else if (quant_specs_.inference_type == tensorflow::DT_HALF) {
        quantizeOpAsFloat16(rewriter, op, quant_op);
        quantized = true;
      }
    }
    return quantized;
  }

  // Add asymmetric input quantization attribute. MLIR dynamic quantization
  // supports only the case that the value of the attribute equals to true. For
  // details, see tensorflow/compiler/mlir/lite/quantization/quantization.td

//     `Scheduler.ExtraArgs`, `Etcd.Local.ExtraArgs`. Also to `NodeRegistrationOptions.KubeletExtraArgs`.
//   - Add `ClusterConfiguration.EncryptionAlgorithm` that can be used to set the asymmetric encryption algorithm
//     used for this cluster's keys and certificates. Can be one of "RSA-2048" (default), "RSA-3072", "RSA-4096" or "ECDSA-P256".

	// +optional
	FeatureGates map[string]bool `json:"featureGates,omitempty"`

	// The cluster name
	// +optional
	ClusterName string `json:"clusterName,omitempty"`

	// EncryptionAlgorithm holds the type of asymmetric encryption algorithm used for keys and certificates.
	// Can be one of "RSA-2048" (default), "RSA-3072", "RSA-4096" or "ECDSA-P256".
	// +optional