// 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.
  }];

                @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.

      attr_map = "strides:0,use_cudnn_on_gpu:1,padding:2,explicit_paddings:3,dilations:4"
    } : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
    func.return %3 : tensor<*xf32>
  }

  // TODO(b/269387785): Support asymmetric quantization for weight with weight-only scheme.
  parameters[
    {"quantized_ops": ["MatMul"], "internal_func_name": "internal_matmul_fn"},
    {"quantized_ops": ["Conv2D"], "internal_func_name": "internal_conv2d_fn"},

### Replication of DeleteMarker and versioned Delete

	// the message passed to Signer.Sign, or else zero to indicate that no
	// hashing was done.
	HashFunc() Hash
}

// Decrypter is an interface for an opaque private key that can be used for
// asymmetric decryption operations. An example would be an RSA key
// kept in a hardware module.
type Decrypter interface {
	// Public returns the public key corresponding to the opaque,
	// private key.
	Public() PublicKey

  } 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) {