@Test
                public void test() {
                    FileInfo expected = new FileInfo("/path/from", new byte[]{ 0x0 });
                    throw new AssertionFailedError(
                        "Asymmetric expected and actual objects",
                        expected,
                        "actual content"
                    );
                }
            }
        '''

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.

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.

### Replication of DeleteMarker and versioned Delete

  } 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) {

				{IP: "1.1.1.1"},
				{IP: "::1"},
			},
		},
		// v6 and v4
		{
			PodIP: "::1",
			PodIPs: []v1.PodIP{
				{IP: "::1"},
				{IP: "1.1.1.1"},
			},
		},
	}

	// run asymmetric cases
	for _, tc := range asymmetricInputs {
		testInput := tc.in

		corePodStatus := core.PodStatus{}
		// convert..

        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.return %mul : tensor<*xf32>
  }

  // TODO(b/263199401): Support quantization options for activation quantization for DRQ
  // Note: following function supports per-tensor, asymmetric, non_narrow_range.
  func.func private @internal_calculate_quant_params(%input : tensor<*xf32>) -> (tensor<1xf32>, tensor<1xi32>) {
    %zero = "tf.Const"() {value = dense<0.0> : tensor<1xf32>} : () -> tensor<1xf32>

      } 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