tbl_outs = [
        (
            ["-gen-rewriters"],
            "generated_canonicalize.inc",
        ),
    ],
    tblgen = "@llvm-project//mlir:mlir-tblgen",
    td_file = "canonicalize.td",
    deps = [
        ":rewrite_util_td_files",
        "//tensorflow/compiler/mlir/tensorflow:tensorflow_ops_td_files",
    ],
)

gentbl_cc_library(
    name = "tensorflow_reduce_patterns_inc_gen",

// (unlike the rest), since this is the primary use case for such ops and
// verification of shapes and broadcasts is desired.
// RUN: tf-opt "-xla-legalize-tf=legalize-chlo=true" -canonicalize %s | FileCheck %s
// RUN: tf-opt "-xla-legalize-tf=legalize-chlo=false" %s | FileCheck --check-prefix CHLO %s

//===----------------------------------------------------------------------===//
// Binary op legalizations.

      if (failed(result)) return WalkResult::interrupt();
      return WalkResult::advance();
    });
  });

  if (walk_result.wasInterrupted()) return signalPassFailure();

  // Clean up and canonicalize to remove dead local variables as some local
  // variables might be dead after hoisting resource loads/stores.
  if (failed(TF::CleanupAndCanonicalizeForResourceOpLifting(module)))
    return signalPassFailure();
}

			}
		}
	}()

	kubeletPid := os.Getpid()
	mountSource := fmt.Sprintf("/proc/%d/fd/%v", kubeletPid, fd)

	// Do the bind mount
	options := []string{"bind"}
	mountFlags := []string{"--no-canonicalize"}
	klog.V(5).Infof("bind mounting %q at %q", mountSource, bindPathTarget)

unset CDPATH

USER_ID=$(id -u)
GROUP_ID=$(id -g)

DOCKER_OPTS=${DOCKER_OPTS:-""}
IFS=" " read -r -a DOCKER <<< "docker ${DOCKER_OPTS}"
DOCKER_HOST=${DOCKER_HOST:-""}
GOPROXY=${GOPROXY:-""}

# This will canonicalize the path
KUBE_ROOT=$(cd "$(dirname "${BASH_SOURCE[0]}")"/.. && pwd -P)

source "${KUBE_ROOT}/hack/lib/init.sh"

# Constants
readonly KUBE_BUILD_IMAGE_REPO=kube-build

type Header map[string][]string

// Get gets the first value associated with the given key.
// It is case insensitive; CanonicalMIMEHeaderKey is used
// to canonicalize the provided key.
// If there are no values associated with the key, Get returns "".
// To access multiple values of a key, or to use non-canonical keys,
// access the map directly.
func (h Header) Get(key string) string {

	i int64Amount
	// d is the quantity in inf.Dec form if d.Dec != nil
	d infDecAmount
	// s is the generated value of this quantity to avoid recalculation
	s string

	// Change Format at will. See the comment for Canonicalize for
	// more details.
	Format
}

// CanonicalValue allows a quantity amount to be converted to a string.
type CanonicalValue interface {

    auto output_tensor_type = output_type.dyn_cast<TensorType>();
    if (!input_tensor_type || !output_tensor_type) {
      return failure();
    }

    // Canonicalize two tfr.cast pairs with different element type to
    // two tfr.casts with the same element type followed by a tf.Cast.
    if ((input_tensor_type.getElementType() !=
         output_tensor_type.getElementType()) &&

      has_ref_vars_(has_ref_vars) {}

void XlaLocalLaunchBase::ComputeAsync(OpKernelContext* ctx, DoneCallback done) {
  VLOG(1) << "XlaLocalLaunchOpBase::Compute "
          << Canonicalize(function_.name(), AttrSlice(&function_.attr()));
  xla_launch_counter->GetCell(platform_info_.device_type().type_string())
      ->IncrementBy(1);

  std::vector<const Tensor*> inputs = InputsFromContext(ctx);

// RUN: tf-opt -convert-tf-quant-types -quant-convert-tf-quant-ops-to-mhlo -canonicalize "-xla-legalize-tf=legalize-chlo=false" -split-input-file %s | FILECHECK_OPTS="" FileCheck %s


//===----------------------------------------------------------------------===//
// tf.UniformQuantizedDotHybrid legalization
//===----------------------------------------------------------------------===//

// CHECK-LABEL: func @quantized_matmul_fn