# Mapping of signature def key -> SignatureDef.
_SignatureDefMap = Mapping[str, meta_graph_pb2.SignatureDef]

# Default minimum number of elements in the weights for them to be quantized
# during dynamic range quantization (DRQ) and weight-only quantization.
_DYNAMIC_RANGE_DEFAULT_MIN_NUM_ELEMENTS_FOR_WEIGHTS = 1024


def _is_qat_saved_model(saved_model_path: str):

  // broadcasting support. This needs to be run immediately after HLO->TF
  // legalization; otherwise other passes like `ConvertTFBroadcastTo` will
  // constant fold the newly generated TF broadcast ops and materialize the
  // weights.
  pass_manager.addNestedPass<mlir::func::FuncOp>(
      mlir::TF::CreateBroadcastFoldPass());

  // Canonicalization after TF legalization.
  pass_manager.addNestedPass<mlir::func::FuncOp>(

                        if a sidecar is present in the workload.
                      type: string
                    weight:
                      description: The load balancing weight associated with the endpoint.
                      maximum: 4294967295
                      minimum: 0
                      type: integer
                  type: object

      ValueReference<K, V> previous = entry.getValueReference();
      int weight = map.weigher.weigh(key, value);
      checkState(weight >= 0, "Weights must be non-negative");

      ValueReference<K, V> valueReference =
          map.valueStrength.referenceValue(this, entry, value, weight);
      entry.setValueReference(valueReference);
      recordWrite(entry, weight, now);
      previous.notifyNewValue(value);
    }

                        if a sidecar is present in the workload.
                      type: string
                    weight:
                      description: The load balancing weight associated with the endpoint.
                      maximum: 4294967295
                      minimum: 0
                      type: integer
                  type: object

      ValueReference<K, V> previous = entry.getValueReference();
      int weight = map.weigher.weigh(key, value);
      checkState(weight >= 0, "Weights must be non-negative");

      ValueReference<K, V> valueReference =
          map.valueStrength.referenceValue(this, entry, value, weight);
      entry.setValueReference(valueReference);
      recordWrite(entry, weight, now);
      previous.notifyNewValue(value);
    }

        enable_per_channel_quantized_weight_ &&
            IsWeightPerChannelQuantized(quantization_method));
  }

  // Returns true if the quantization method indicates per-channel quantization
  // for convolution weights. This method specifically matches a quantization
  // dimension of 3 for the input index 1 or unspecified quantization dimension
  // for the input index 1.

                            type: integer
                          weight:
                            default: 1
                            description: |-
                              Weight specifies the proportion of requests forwarded to the referenced
                              backend. This is computed as weight/(sum of all weights in this

        `tf.keras.mixed_precision.experimental.LossScaleOptimizer`, the weights
        of the `DynanmicLossScale` are copied into the `LossScaleOptimizer`
        instead of being reused. This means modifying the weights of the
        `DynamicLossScale` will no longer affect the weights of the
        LossScaleOptimizer, and vice versa.

            // input. Only int8 weight is supported for now.
            inputs.push_back(dq_op.getOperand());
            is_operand_or_result_modified = true;
          } else {
            // Otherwise, it's the case where the operand is activations or the
            // quantizing_op is non-supported/weight-only.
            inputs.push_back(operand);
          }
        } else {