A SavedModel object with TF quantization applied.

  Raises:
    ValueError: when the model is QAT model.
  """
  mode_str = 'weight-only quantization'

  # QAT weight-only is not supported yet.
  if _is_qat_saved_model(src_saved_model_path):
    raise ValueError(
        'The models trained with quantization-aware training (QAT) is not '
        'supported for %s.' % mode_str
    )

  logging.info(

apiVersion: release-notes/v2
kind: feature
area: security

releaseNotes:
- |

	"fmt"
	"strconv"
	"sync"
	"sync/atomic"
	"time"

	cryptomb "github.com/envoyproxy/go-control-plane/contrib/envoy/extensions/private_key_providers/cryptomb/v3alpha"
	qat "github.com/envoyproxy/go-control-plane/contrib/envoy/extensions/private_key_providers/qat/v3alpha"
	core "github.com/envoyproxy/go-control-plane/envoy/config/core/v3"
	tls "github.com/envoyproxy/go-control-plane/envoy/extensions/transport_sockets/tls/v3"

	"strings"
	"time"

	xxhashv2 "github.com/cespare/xxhash/v2"
	cryptomb "github.com/envoyproxy/go-control-plane/contrib/envoy/extensions/private_key_providers/cryptomb/v3alpha"
	qat "github.com/envoyproxy/go-control-plane/contrib/envoy/extensions/private_key_providers/qat/v3alpha"
	core "github.com/envoyproxy/go-control-plane/envoy/config/core/v3"
	envoytls "github.com/envoyproxy/go-control-plane/envoy/extensions/transport_sockets/tls/v3"

limitations under the License.
==============================================================================*/
//
// This file defines support utilities for interoperating with FakeQuant* based
// QAT (Quantized Aware Training) computations, as implemented by TFLite. Note
// that FakeQuant* operators mix multiple concerns specific to how TFLite
// originally implemented quantization. As such, utilities here enforce

						},
					}
				},
			),
			isValid: true,
		},
		{
			name: "private key provider with qat without poll_delay",
			in: modify(valid,
				func(c *meshconfig.ProxyConfig) {
					c.PrivateKeyProvider = &meshconfig.PrivateKeyProvider{
						Provider: &meshconfig.PrivateKeyProvider_Qat{
							Qat: &meshconfig.PrivateKeyProvider_QAT{},
						},
					}
				},
			),
			isValid: false,
		},
		{

  bool post_training_quantization = false;

  // Whether to allow dynamic range quantization. This is the easiest
  // quantization mode which doesn't require QAT or sample inputs.
  // This option only targets `DT_HALF` and `DT_QINT8` inference type.
  bool weight_quantization = false;

  // Whether to use the MLIR dynamic range quantizer instead of TOCO.

    )
    # Insert fake quant to simulate a QAT model.
    weight = array_ops.fake_quant_with_min_max_args(
        weight, min=-0.1, max=0.2, num_bits=8, narrow_range=False
    )

    # shape: (2, 2)
    output_tensor = math_ops.matmul(matmul_input, weight)
    # Insert fake quant to simulate a QAT model.
    output_tensor = array_ops.fake_quant_with_min_max_args(

            signature_def_map);

        return absl::OkStatus();
      },
      R"pbdoc(
      Quantizes a model that went through quantization-aware training (QAT)
      saved at `src_saved_model_path`. The resulting model will be saved to
      `dst_saved_model_path`. Returns an OK sataus when successful, otherwise
      raises `StatusNotOk` exception.

    } else {
      quant_specs->inference_type = DT_QINT8;
      quant_specs->inference_input_type = DT_QINT8;
    }
  } else {
    // These flags are incompatible with post_training_quantize() as only
    // QAT models can provide required ranges.
    quant_specs->disable_infer_tensor_range =
        toco_flags.disable_infer_tensor_range();
    quant_specs->use_fake_quant_num_bits = toco_flags.use_fake_quant_num_bits();
  }