"@local_xla//xla:executable_run_options",
    "@local_xla//xla:status_macros",
    "@local_xla//xla:statusor",
    "@local_xla//xla/client:client_library",
    "@local_xla//xla/client:local_client",
    "@local_xla//xla/service:compiler",
    "@local_xla//xla/service/gpu:gpu_executable_run_options",
    "//tensorflow/core:core_cpu_internal",
    "//tensorflow/core:framework",
    "//tensorflow/core:lib",

#include "tensorflow/compiler/mlir/op_or_arg_name_mapper.h"
#include "tensorflow/compiler/tf2xla/xla_context.h"
#include "tensorflow/compiler/tf2xla/xla_expression.h"
#include "xla/client/xla_builder.h"
#include "xla/client/xla_computation.h"
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"
#include "tensorflow/core/common_runtime/device_mgr.h"
#include "tensorflow/core/framework/op_kernel.h"

namespace mlir {
namespace mhlo {

#include "tensorflow/compiler/jit/xla_compiler_options_util.h"

#include "xla/pjrt/pjrt_client.h"
#include "tensorflow/core/framework/function.h"
#include "tsl/framework/device_id_utils.h"

namespace tensorflow {
namespace {
using XlaDeviceCompiler =
    DeviceCompiler<xla::LocalExecutable, xla::LocalClient>;
using PjRtDeviceCompiler =
    DeviceCompiler<xla::PjRtLoadedExecutable, xla::PjRtClient>;

#include "mlir/Pass/Pass.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tf2xla/api/v2/device_type.pb.h"
#include "tensorflow/compiler/tf2xla/xla_helpers.h"
#include "xla/client/compile_only_client.h"
#include "xla/pjrt/compile_options.pb.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/tpu/kernels/tpu_compile.pb.h"
#include "tensorflow/core/tpu/kernels/tpu_compile_op_support.h"

#include "llvm/MC/TargetRegistry.h"
#include "llvm/Target/TargetMachine.h"
#include "llvm/Target/TargetOptions.h"
#include "llvm/TargetParser/Triple.h"
#include "xla/service/llvm_ir/llvm_type_conversion_util.h"
#include "xla/util.h"

namespace tensorflow {
namespace tfcompile {

using xla::llvm_ir::AsStringRef;

static void AddEmbeddedProtocolBufferToLlvmModule(
    llvm::Module* module, const ::tensorflow::protobuf::MessageLite& proto,

        "@llvm-project//llvm:Support",
        "@llvm-project//mlir:ArithDialect",
        "@llvm-project//mlir:IR",
        "@llvm-project//mlir:Support",
        "@llvm-project//mlir:TransformUtils",
        "@local_xla//xla/mlir_hlo",
    ],
)

cc_library(
    name = "scatter",
    srcs = [
        "scatter.cc",
    ],
    hdrs = [
        "scatter.h",
    ],
    deps = [
        ":util",

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

// Registers the XLA_GPU device, which is an XlaDevice instantiation that runs
// operators using XLA via the XLA "CUDA" or "ROCM" (GPU) backend.

#include <array>
#include <set>

#include "absl/memory/memory.h"
#include "absl/strings/numbers.h"
#include "absl/strings/str_split.h"
#include "tensorflow/compiler/jit/defs.h"

        func_name_attrs(func_name_attrs),
        node(node),
        host_compute_core(host_compute_core) {}
  // XLA cluster name. It might be different from `func_name`.
  const string cluster_name;
  // Name and attributes of XLA computation function.
  const NameAttrList func_name_attrs;
  // The XLA computation node in the graph.
  Node* node;
  // A mapping from outside compilation cluster name to its device assignment.

#include "tensorflow/compiler/mlir/lite/transforms/passes.h"
#include "tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_config.h"
#include "xla/service/hlo.pb.h"
#include "xla/service/hlo_parser.h"
#include "xla/translate/hlo_to_mhlo/hlo_to_mlir_hlo.h"
#include "tensorflow/core/framework/graph.pb.h"
#include "tensorflow/core/framework/graph_debug_info.pb.h"
#include "tensorflow/core/framework/types.pb.h"

        "@llvm-project//mlir:FuncDialect",
        "@llvm-project//mlir:IR",
        "@local_tsl//tsl/platform:errors",
        "@local_xla//xla:shape_util",
        "@local_xla//xla/mlir_hlo:hlo_dialect_registration",
        "@local_xla//xla/translate/mhlo_to_hlo:type_to_shape",
    ],