}
                bulkBuilder.add((DeleteRequestBuilder) builder);
            }
        }
        final RequestOptionCall<BulkRequestBuilder> builderCall = bulkList.getCall();
        if (builderCall != null) {
            builderCall.callback(bulkBuilder);
        }

        final BulkResponse response = bulkBuilder.execute().actionGet(bulkTimeout);

                quant_params, legacy_float_scale, adjusted_quant_dim))) {
      return quant::UniformQuantizedType::get(
          qtype.getFlags(), qtype.getStorageType(), qtype.getExpressedType(),
          qtype.getScale() * scale, qtype.getZeroPoint(),
          qtype.getStorageTypeMin(), qtype.getStorageTypeMax());
    }
    return {};
  };
}

// Returns quantization spec for LSTMs based on their operator properties.

  Value scale_op = rewriter.create<TF::ConstOp>(
      loc, scale_type,
      DenseFPElementsAttr::get(scale_type,
                               {static_cast<float>(qtype.getScale())}));

  if (original_input_tensor_type.getElementType().isBF16()) {
    // Add bf16 cast op after scale to match with the next op's data
    // type.
    scale_op = rewriter.create<TF::CastOp>(

                }
                bulkBuilder.add((DeleteRequestBuilder) builder);
            }
        }
        final RequestOptionCall<BulkRequestBuilder> builderCall = bulkList.getCall();
        if (builderCall != null) {
            builderCall.callback(bulkBuilder);
        }

        final BulkResponse response = bulkBuilder.execute().actionGet(bulkTimeout);

                // The get call is the explicit receiver of this operation call
                val getCall = operationCall.explicitReceiver as? FirFunctionCall ?: return null
                val getPartiallyAppliedSymbol = getCall.toPartiallyAppliedSymbol(arrayAccessExpression.arrayExpression) ?: return null
                CompoundArrayAccessPartiallyAppliedSymbols(

import org.jetbrains.kotlin.resolve.calls.smartcasts.DataFlowValueFactoryImpl
import org.jetbrains.kotlin.resolve.calls.tower.NewAbstractResolvedCall
import org.jetbrains.kotlin.resolve.calls.util.getCall
import org.jetbrains.kotlin.resolve.calls.util.getResolvedCall
import org.jetbrains.kotlin.resolve.diagnostics.Diagnostics
import org.jetbrains.kotlin.resolve.scopes.receivers.ExpressionReceiver

  Type accumulation_quantized_element_type;
  TensorType new_gemm_style_op_result_type;

  const double input_scale =
      mlir::cast<UniformQuantizedType>(getElementTypeOrSelf(input_type))
          .getScale();

  if (enable_per_channel_quantized_weight) {
    ArrayRef<double> filter_scales = mlir::cast<UniformQuantizedPerAxisType>(
                                         getElementTypeOrSelf(filter_type))

	StatefulSetPodNameLabel        = "statefulset.kubernetes.io/pod-name"
	PodIndexLabel                  = "apps.kubernetes.io/pod-index"
)

// +genclient
// +genclient:method=GetScale,verb=get,subresource=scale,result=k8s.io/api/autoscaling/v1.Scale
// +genclient:method=UpdateScale,verb=update,subresource=scale,input=k8s.io/api/autoscaling/v1.Scale,result=k8s.io/api/autoscaling/v1.Scale

  auto bias_op = op->getOperand(bias_index).getDefiningOp<arith::ConstantOp>();
  const double input_scale =
      mlir::cast<UniformQuantizedType>(input_state.params).getScale();

  auto bias_values = mlir::cast<DenseFPElementsAttr>(bias_op.getValue());
  // Restrict maximum absolute value of bias within INT_MAX / 2, to make some
  // room for accumulator.

	// +optional
	Status ScaleStatus `json:"status,omitempty" protobuf:"bytes,3,opt,name=status"`
}

// +genclient
// +genclient:method=GetScale,verb=get,subresource=scale,result=Scale
// +genclient:method=UpdateScale,verb=update,subresource=scale,input=Scale,result=Scale
// +genclient:method=ApplyScale,verb=apply,subresource=scale,input=Scale,result=Scale