context->SetStatus(
      errors::InvalidArgument("Variable: ", name_, " not configured"));
}
}  // namespace

REGISTER_OP(tfcompile::kXlaAotOnlyVarHandleOp)
    .Doc(R"doc(
Internal VarHandleOp registration used for XLA AOT compilation.
)doc")
    .Attr("container: string = ''")
    .Attr("shared_name: string = ''")
    .Attr("debug_name: string = ''")
    .Attr("dtype: type")
    .Attr("shape: shape")
    .Output("resource: resource")

#include "tensorflow/compiler/tf2xla/xla_expression.h"
#include "tensorflow/compiler/tf2xla/xla_helpers.h"
#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
#include "xla/client/xla_builder.h"
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"
#include "tensorflow/core/common_runtime/device.h"
#include "tensorflow/core/common_runtime/device_factory.h"
#include "tensorflow/core/common_runtime/device_mgr.h"

    // ShapeRepresentationFn). Each bundle describes how the on-host shapes of
    // a) argument and return value, for entry computations b) variables, for
    // all computations, should be represented in XLA. Parameters/return values
    // will be shaped according to the function pair, and reshaped back to/from
    // their declared shapes for computations. Must be non-empty.
    std::vector<XlaShapeLayoutHelpers::ShapeDeterminationFns>

#include "mlir/Transforms/DialectConversion.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/quantization/stablehlo/utils/tf_type_utils.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"

namespace mlir::quant::stablehlo {
namespace {

using quant::tensorflow::IsTFQintType;
using quant::tensorflow::IsTFUniformQuantizedOp;

#define GEN_PASS_DEF_VERIFYQUANTLEGALIZATION

    'tensorflow/compiler/mlir/quantization/tensorflow',
    'tensorflow/compiler/mlir/tensorflow',
    'tensorflow/compiler/mlir/tfrt',
    'tensorflow/compiler/mlir/tools/kernel_gen',
    os.path.join(external_srcdir, 'local_xla/xla/mlir_hlo'),
    'tensorflow/core/ir/importexport/',
    'tensorflow/core/ir/tests/',
    'tensorflow/core/transforms/',
    'tensorflow/dtensor/mlir/tests',
]
config.mlir_tf_tools_dirs = [

limitations under the License.
==============================================================================*/

#ifndef TENSORFLOW_C_TF_STATUS_H_
#define TENSORFLOW_C_TF_STATUS_H_

#include "tensorflow/c/c_api_macros.h"
#include "xla/tsl/c/tsl_status.h"

#ifdef __cplusplus
extern "C" {
#endif

typedef struct TSL_Status TF_Status;

// --------------------------------------------------------------------------

#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "mlir/Transforms/DialectConversion.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/lite/transforms/passes.h"  // IWYU pragma: keep
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"  // IWYU pragma: keep

namespace mlir {
namespace odml {

// Ensure an attribute named attr_name exists and it is of type AttrType.

// converting them to NHWC formats along with extra optimizations such as
// constant folding the transpose->convolution pattern. This is useful when
// downstream pipeline (e.g. XLA) is more optimized when accepting NHWC formats.
void AddProcessNchwTensorPasses(OpPassManager& pm);

// Registers quantization pass pipelines. This is only required when running

#include "tensorflow/compiler/mlir/quantization/tensorflow/cc/run_passes.h"
#include "tensorflow/compiler/mlir/quantization/tensorflow/python/py_function_lib.h"
#include "xla/mlir_hlo/mhlo/transforms/passes.h"
#include "tsl/platform/errors.h"
#include "tsl/platform/statusor.h"

namespace mlir::quant::stablehlo {

using ::stablehlo::quantization::GetReportFilePath;

}

absl::StatusOr<ResourceMgr*> GetResourceMgrForDeviceCompiler(
    const OpKernelContext& ctx, const DeviceType& device_type) {
  // We store information about the JIT-compiled XLA computation in the
  // ResourceMgr. The DeviceCompiler (which contains the DeviceCompilationCache)
  // is stored in the tfrt_global ResourceMgr for TPU and the Device ResourceMgr