ArrayRef<int64_t> shape, int64_t val) {
  RankedTensorType type =
      tensorflow::GetTypeFromTFTensorShape(shape, rewriter->getIntegerType(64));
  DenseElementsAttr attr =
      DenseElementsAttr::get(type, rewriter->getI64IntegerAttr(val));
  return rewriter->create<arith::ConstantOp>(loc, type, attr);
}

Value CreateI32SplatTensor(Location loc, PatternRewriter *rewriter,

				Rewrite: *filter.Path.ReplaceFullPath,
			}
		}
	}
	if filter.Hostname != nil {
		rewrite.Authority = string(*filter.Hostname)
	}
	// Nothing done
	if rewrite.Uri == "" && rewrite.UriRegexRewrite == nil && rewrite.Authority == "" {
		return nil
	}
	return rewrite
}

func createRedirectFilter(filter *k8s.HTTPRequestRedirectFilter) *istio.HTTPRedirect {
	if filter == nil {

 * When such a classloader attempts to define a class, the agent calls {@link #instrumentClass(String, ProtectionDomain, byte[])} method
 * giving the classloader a chance to rewrite the bytecode of the class.
 * <p>
 * Methods defined in this interface may be called concurrently in multiple threads.
 */
public interface InstrumentingClassLoader {
    /**

#include "absl/types/optional.h"
#include "tensorflow/compiler/jit/encapsulate_util.h"
#include "xla/status_macros.h"
#include "tensorflow/core/graph/graph.h"

namespace tensorflow {

// Rewrite function for outside compilation subgraphs. It will perform the
// following steps:
//
// 1. Add a XLA computation key placeholder node (it will be used as input for
//    XlaRecvAtHost and XlaSendFromHost);

    private final List<ProjectSegment> items;

    public ProjectBuildList(List<ProjectSegment> items) {
        this.items = Collections.unmodifiableList(items);
    }

    // TODO Optimize; or maybe just rewrite the whole way aggregating mojos are being run.
    /**
     * Returns aProjectBuildList that contains only items for the specified taskSegment
     * @param taskSegment the requested task segment

        return success();
      }
    }
    return failure();
  }

  void rewrite(Operation* op, PatternRewriter& rewriter) const override {
    // Casts inputs of the operation.
    for (int i = 0; i < op->getNumOperands(); i++) {
      Value input = op->getOperand(i);
      if (getElementTypeOrSelf(input).isBF16()) {
        Value f32_cast = rewriter.create<TF::CastOp>(
            op->getLoc(),

					f.FuncFlag |= abi.FuncFlagSPWrite
					if ctxt.Debugvlog || !ctxt.IsAsm {
						ctxt.Logf("auto-SPWRITE: %s %v\n", cursym.Name, p)
						if !ctxt.IsAsm {
							ctxt.Diag("invalid auto-SPWRITE in non-assembly")
							ctxt.DiagFlush()
							log.Fatalf("bad SPWRITE")
						}
					}
				}
			}
			continue

		case APUSHL, APUSHFL:
			deltasp += 4
			p.Spadj = 4

  void createNewDequantizeOp(PatternRewriter& rewriter,
                             TF::PartitionedCallOp original_dequantize_op,
                             Operation* user_op, int user_idx,
                             Type new_user_op_type) const {
    auto op_before_dequantize = original_dequantize_op.getOperand(0);

    // Create a new dequantize op that is propagated.
    rewriter.setInsertionPointAfter(user_op);

#include "tensorflow/compiler/mlir/tensorflow/transforms/passes.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/attribute_utils.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/call_graph_util.h"

#define DEBUG_TYPE "tf-xla-rewrite"

namespace mlir {
namespace {

#define GEN_PASS_DEF_XLAREWRITEPASS
#include "tensorflow/compiler/mlir/tensorflow/transforms/tf_device_passes.h.inc"

    // Erase unused ops after the transformation.
    rewriter.eraseOp(output_uniform_dequantize_call_pattern->GetCallOp());
    rewriter.eraseOp(output_uniform_quantize_call_pattern->GetCallOp());
    rewriter.eraseOp(multiply_op);
    rewriter.eraseOp(subtract_op);
    rewriter.eraseOp(input_i8_to_f32_convert_op);
    rewriter.eraseOp(input_uniform_quantize_call_pattern->GetCallOp());
  }