// the rules in late lower run after the general rules.
	{"lower", "late lower"},
	// late lower may generate some values that need to be CSEed.
	{"late lower", "lowered cse"},
	// checkLower must run after lowering & subsequent dead code elim
	{"lower", "checkLower"},
	{"lowered deadcode", "checkLower"},
	{"late lower", "checkLower"},
	// late nilcheck needs instructions to be scheduled.
	{"schedule", "late nilcheck"},

  // TODO(b/157054714): When a better abstraction instead of _TPUCompileMlirOp
  // and _XlaRecvAtHostOp and _XlaSendFromHostOp are used, update to a more
  // structured lowering.
  old_parallel_execute.walk(
      [&](TF::_XlaCompileMlirPlaceholderProgramKeyOp key_op) {
        key_op.replaceAllUsesWith(compile_op->getResult(1));
        key_op.erase();
      });

template <typename UniformQuantizedConvolutionOp>
FailureOr<SmallVector<NamedAttribute>> ConvertToMhloConvolutionOpAttrs(
    UniformQuantizedConvolutionOp op, PatternRewriter &rewriter) {
  // TODO(b/261005147): Update the lowering logic after migration to mhlo
  // ConvolutionDimensionNumbers.
  ::tensorflow::UniformQuantizedConvolutionDimensionNumbersAttr dnums_input;
  if (!dnums_input.ParseFromString(std::string(op.getDimensionNumbers()))) {

  float half_range = is_unsigned ? 0 : 128;
  return GetScalarOfType(builder->getF32Type(), half_range);
}

// Returns reduction indices to use while lowering tf.BiasAddGrad op to tf.Sum
// op.
DenseIntElementsAttr GetBiasAddGradReductionIndices(int64_t rank,
                                                    StringAttr data_format,

#include "tensorflow/compiler/aot/tests/test_graph_tfvariable_readonly_mlir_bridge.h"
#include "tensorflow/compiler/aot/tests/test_graph_tfvariable_sequential_updates_mlir_bridge.h"
// Similarly, there are files for testing the MLIR based lowering of HLO to
// object code for XLA:CPU
#elif defined(MHLO_LOWERING_TEST)
#include "tensorflow/compiler/aot/tests/test_graph_tfadd_mhlo_lowering.h"
#include "tensorflow/compiler/aot/tests/test_graph_tfadd_with_ckpt_mhlo_lowering.h"

    This op is rewritten to generic ops that perform the scale and shift
    and can operate on non-quantized types.

    Currently, TF_DequantizeOp is the only op with a lowering that falls
    in this category. When more lowerings are added (e.g. QuantizeV2Op),
    they should be added to this pass.
  }];

  let constructor = "TF::CreateLowerQuantizedPass()";
}

		{name: "LoweredAtomicOr32", argLength: 3, reg: gpatomic, asm: "AMOORW", faultOnNilArg0: true, hasSideEffects: true},

		// Lowering pass-throughs
		{name: "LoweredNilCheck", argLength: 2, faultOnNilArg0: true, nilCheck: true, reg: regInfo{inputs: []regMask{gpspMask}}}, // arg0=ptr,arg1=mem, returns void.  Faults if ptr is nil.

		t.Fatal(err)
	}

	// Get the key for the controller
	rsKey, err := controller.KeyFunc(rsSpec)
	if err != nil {
		t.Errorf("Couldn't get key for object %#v: %v", rsSpec, err)
	}

	// Lowering expectations should lead to a sync that creates a replica, however the
	// fakePodControl error will prevent this, leaving expectations at 0, 0
	manager.expectations.CreationObserved(logger, rsKey)
	rsSpec.Status.Replicas = 1

See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

// This file implements logic for lowering TensorFlow dialect to XLA dialect.
#include <algorithm>
#include <cctype>
#include <cmath>
#include <cstddef>
#include <cstdint>
#include <iterator>
#include <limits>
#include <numeric>
#include <optional>

This op is rewritten to generic ops that perform the scale and shift
and can operate on non-quantized types.

Currently, TF_DequantizeOp is the only op with a lowering that falls
in this category. When more lowerings are added (e.g. QuantizeV2Op),
they should be added to this pass.
### `-tf-mark-ops-for-outside-compilation`