MLIRContext* ctx = &getContext();
  RewritePatternSet patterns(ctx);
  mhlo::populateUnfuseBatchNormPatterns(ctx, &patterns);

  if (failed(
          applyPatternsAndFoldGreedily(getOperation(), std::move(patterns)))) {
    return signalPassFailure();
  }
}
}  // namespace

  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(IdentifyDilatedConvPass)
  void runOnOperation() override;
};

void IdentifyDilatedConvPass::runOnOperation() {
  RewritePatternSet patterns(&getContext());
  auto func = getOperation();

  patterns.add<ConvertTFDilatedConvOp<TF::Conv2DOp>,
               ConvertTFDilatedConvOp<TF::DepthwiseConv2dNativeOp>>(
      &getContext());
  (void)applyPatternsAndFoldGreedily(func, std::move(patterns));
}

    return "Add remarks based on cost analysis for testing purpose.";
  }
  void runOnOperation() override {
    const auto& cost_analysis = getAnalysis<CostAnalysis>();

    auto func_op = getOperation();
    for (auto& op : func_op.front()) {
      op.emitRemark() << "Cost: " << cost_analysis.GetCost(&op);
    }
  }
};

static mlir::PassRegistration<TestCostAnalysis> pass;

}  // namespace tfrt_compiler

  void runOnOperation() override {
    RewritePatternSet patterns(&getContext());
    mlir::TF::PopulateTFDataOptimizationPatterns(&getContext(), &patterns);

    (void)applyPatternsAndFoldGreedily(getOperation(), std::move(patterns));
  }
};

}  // namespace

std::unique_ptr<OperationPass<func::FuncOp>> CreateTFDataOptimizationPass() {
  return std::make_unique<TFDataOptimization>();
}

}  // namespace TF

    }
    // Use the min/max from the operands and the num_bits and narrow_range
    // attribute to create the quantization parameter for the new quantize op.
    rewriter.setInsertionPointAfter(tf_op.getOperation());
    IntegerAttr num_bits = rewriter.getI64IntegerAttr(tf_op.getNumBits());
    BoolAttr narrow_range = rewriter.getBoolAttr(tf_op.getNarrowRange());
    Type res_type = tf_op.getType();

#include "tensorflow/compiler/mlir/quantization/stablehlo/passes/bridge/optimize.inc"

void OptimizeIntGraph::runOnOperation() {
  RewritePatternSet patterns(&getContext());
  populateWithGenerated(patterns);
  auto func = getOperation();
  if (failed(applyPatternsAndFoldGreedily(func, std::move(patterns)))) {
    signalPassFailure();
  }
}

}  // namespace

std::unique_ptr<OperationPass<func::FuncOp>> CreateOptimizeIntGraphPass() {

 public:
  void runOnOperation() override;
};

void InferenceMetricsPass::runOnOperation() {
  bool has_tpu_partitioned_call = false;
  ModuleOp module = getOperation();

  for (auto func_op : module.getOps<func::FuncOp>()) {
    func_op->walk(
        [&](TF::TPUPartitionedCallOp op) { has_tpu_partitioned_call = true; });

    if (has_tpu_partitioned_call) break;
  }

						// Ignore context cancelled from webhook metrics
						if !errors.Is(err.Reason, context.Canceled) {
							admissionmetrics.Metrics.ObserveWebhookRejection(ctx, hook.Name, "validating", string(versionedAttr.Attributes.GetOperation()), admissionmetrics.WebhookRejectionCallingWebhookError, int(err.Status.ErrStatus.Code))
						}
					}

  // to yield.
  ReplaceRegionWithCall(GetName(while_op.getOperation(), "_cond"),
                        while_op.getCond(), false, num_loop_carried,
                        extern_values, types, while_op.getLoc());
  ReplaceRegionWithCall(GetName(while_op.getOperation(), "_body"),
                        while_op.getBody(), true, num_loop_carried,

        new_ewise_op->getLoc(), op->getResult(0).getType(),
        new_ewise_op->getResults()[0], perm);
    rewriter.replaceOp(op, out_tpose_op.getOperation());
    return success();
  }
};

void PushTransposeThroughEwisePass::runOnOperation() {
  auto module = getOperation();
  RewritePatternSet patterns(&getContext());

  patterns.add<CommuteTransposeWithEwiseOps>(&getContext());