#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/quantization/common/attrs_and_constraints.h"
#include "tensorflow/compiler/mlir/quantization/tensorflow/cc/constant_fold.h"
#include "xla/xla_data.pb.h"
#include "tensorflow/lite/kernels/padding.h"

namespace mlir::quant {
namespace {

Value GetDimValue(OpBuilder &builder, Location loc, Value shape_value,
                  int32_t dim) {

#include "tensorflow/c/tf_datatype.h"
#include "tensorflow/c/tf_status.h"
#include "tensorflow/c/tf_status_helper.h"
#include "tensorflow/c/tf_tensor.h"
#include "tensorflow/c/tf_tensor_internal.h"
#include "xla/tsl/c/tsl_status_internal.h"  // IWYU pragma: keep
#include "tensorflow/core/framework/allocator.h"
#include "tensorflow/core/framework/attr_value.pb.h"
#include "tensorflow/core/framework/attr_value_util.h"

        Eager mode.

*   `tf.lite`:

    *   Enable TFLite experimental new converter by default.

*   XLA

    *   XLA now builds and works on windows. All prebuilt packages come with XLA
        available.
    *   XLA can be
        [enabled for a `tf.function`](https://www.tensorflow.org/xla#explicit_compilation_with_tffunction)
        with “compile or throw exception” semantics on CPU and GPU.

#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/dynamic_shape_utils.h"
#include "xla/test.h"
#include "tensorflow/core/framework/tensor_testutil.h"
#include "tensorflow/core/framework/tensor_util.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/lib/core/errors.h"

#include "tensorflow/compiler/mlir/tensorflow/utils/convert_type.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/export_utils.h"
#include "tensorflow/compiler/mlir/utils/string_container_utils.h"
#include "xla/status_macros.h"
#include "tensorflow/core/framework/node_def_util.h"
#include "tensorflow/core/framework/tensor_shape.pb.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/lib/core/status.h"

          devices.device_names(), num_replicas, num_cores_per_replica,
          topology_attr.getValue(), status_or_device_coodinates.value());
  return success();
}

// Move a broadcast into the XLA cluster, converting it to an XlaAllReduce. This
// skips if the element type is not known to be valid for XlaAllReduce.
LogicalResult MoveBroadcastToCluster(OpBuilder& builder,

#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_dialect.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"

namespace mlir {
namespace TFL {
namespace {
#define GEN_PASS_DEF_REDUCEWHILEOPERANDSPASS
#include "tensorflow/compiler/mlir/lite/transforms/passes.h.inc"

struct ReduceWhileOperandsPass

  // The following ops must be preserved regardless of reachability. Ideally,
  // all graphs should have control dependencies to enforce this.
  VLOG(2) << "Create TF XLA Bridge pipeline";
  pm.addPass(mlir::TFDevice::CreateXlaValidateInputsPass());
  pm.addNestedPass<FuncOp>(
      mlir::TF::CreateCanonicalizeCompileAndReplicateAttributesPass());

        "@llvm-project//mlir:MlirOptLib",
        "@llvm-project//mlir:ShapeDialect",
        "@llvm-project//mlir:Support",
        "@llvm-project//mlir:Transforms",
        "@local_xla//xla/mlir_hlo",
        "@tf_runtime//:init_tfrt_dialects",
        "@tf_runtime//:print_stream_pass",
    ],
)

tf_cc_binary(
    name = "tf-tfrt-opt",
    testonly = True,
    deps = [":tf_tfrt_opt_lib"],

#include "mlir/IR/Matchers.h"  // from @llvm-project
#include "mlir/IR/Value.h"  // from @llvm-project
#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "mlir/Transforms/DialectConversion.h"  // from @llvm-project
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"

namespace mlir {
namespace odml {

namespace {

// Facilitate access to 1-d backing data for a tensor so that values in a 1-d