$dilations, (location $conv)),
      [(CanFuseMulAndConv2D $filter_value, $mul_value), (HasOneUse $conv)]>;

// This rule does the following pattern match and rewrite:
//
//       input     bias                    input  value  bias  value
//          \      /                =>       \    /        \    /
//          BiasAdd    value                   Mul          Mul

          sudo sysctl net.ipv6.conf.default.disable_ipv6=0
          make verify-healing
          make verify-healing-inconsistent-versions
          make verify-healing-with-root-disks

		if err != nil {
			t.Error(err)
			continue
		}
		err = Rewrite(tf, m, newID)
		err2 := tf.Close()
		if err != nil {
			t.Errorf("Rewrite(%s): %v", f, err)
			continue
		}
		if err2 != nil {
			t.Fatal(err2)
		}

		id, err = ReadFile(tmp)
		if id != newID || err != nil {
			t.Errorf("ReadFile(%s after Rewrite) = %q, %v, want %q, nil", f, id, err, newID)
		}

func raceprocdestroy(ctx uintptr)                                           { throw("race") }
func racemapshadow(addr unsafe.Pointer, size uintptr)                       { throw("race") }
func racewritepc(addr unsafe.Pointer, callerpc, pc uintptr)                 { throw("race") }
func racereadpc(addr unsafe.Pointer, callerpc, pc uintptr)                  { throw("race") }

// RUN: tf-tfrt-opt -split-input-file -tf-mlrt-rewrite-ifrt-load-variable %s | FileCheck %s

// Variable is used by both CPU and TPU
//
// CHECK-LABEL: func @serving_default(%arg0: tensor<1x3xf32>) -> tensor<1x1xf32>
// CHECK-NEXT:    [[HANDLE:%.*]] = "tf.VarHandleOp"()
// CHECK-NEXT:    [[ARRAYKEY:%.*]], [[FURTURE:%.*]] = "tf_mlrt.tf_ifrt_load_variable"([[HANDLE]])

		return kubeconfig{}, err
	}

	return kubeconfig{
		Full:     string(fullYaml),
		Redacted: string(redacted),
	}, nil
}

// maybeWriteKubeConfigFile will validate the existing kubeConfig file, and rewrite/replace it if required.
func maybeWriteKubeConfigFile(cfg *config.InstallConfig) error {
	kc, err := createKubeConfig(cfg)
	if err != nil {
		return err
	}

  return IsBounded(updated);
}

// Propagates more refined type by cloning op using the new operands. This
// allows all rewrite patterns that requires refined types to work without
// requiring a rewrite to the conversion pattern. Declarative rewrite pattern
// (DRR) doesn't even support conversion patterns with TableGen.
class TypePropagator : public ConversionPattern {
 public:

include "mlir/Dialect/Func/IR/FuncOps.td"
include "tensorflow/compiler/mlir/lite/ir/tfl_ops.td"

// Both Quantize and Dequantize ops have side effects, so we have to define
// patterns to remove dead ones after the quantization rewrite.
def : Pat<(TFL_QuantizeOp:$op $in, $qt), (replaceWithValue $in), [(HasNoUseOf:$op)]>;

         * @param logLevel the logging level
         * @param message the original message
         * @return the rewritten message or null if this message should be silenced
         */
        @Nullable
        String rewrite(LogLevel logLevel, String message);
    }

    void setMessageRewriter(MessageRewriter messageRewriter);

    void setFallbackBuildOperationId(OperationIdentifier operationIdentifier);

        return messageRewriter;
    }

    @Override
    void log(LogLevel logLevel, Throwable throwable, String message, OperationIdentifier operationIdentifier) {
        final String rewrittenMessage = messageRewriter.rewrite(logLevel, message);
        if (rewrittenMessage == null) {
            return;
        }
        delegate.log(logLevel, throwable, rewrittenMessage, operationIdentifier);
    }

    @Override