//
// (Note that this can be viewed equivalently as:
//
//	actionID(binary)/buildID(main.a)/contentID(binary)
//
// Storing the buildID(main.a) in the middle lets the computations that care
// about the prefix or suffix halves ignore the middle and preserves the
// original build ID as a contiguous string.)
//
// During the build, when it's time to build main.a, the gofmt binary has the

	physPageSize = getPageSize()

	// Windows dynamic priority boosting assumes that a process has different types
	// of dedicated threads -- GUI, IO, computational, etc. Go processes use
	// equivalent threads that all do a mix of GUI, IO, computations, etc.
	// In such context dynamic priority boosting does nothing but harm, so we turn it off.
	stdcall2(_SetProcessPriorityBoost, currentProcess, 1)
}

//go:nosplit

}

void init() {
        SansTypeface = loadfont();
}

// issue 5242
// Cgo incorrectly computed the alignment of structs
// with no Go accessible fields as 0, and then panicked on
// modulo-by-zero computations.

// issue 50987
// disable arm64 GCC warnings
#cgo CFLAGS: -Wno-psabi -Wno-unknown-warning-option

typedef struct {
} foo;

typedef struct {
	int x : 1;
} bar;

  return true;
}

namespace {

// Compiles the XLA computation into executable code.
Status CompileXla(xla::CompileOnlyClient* client,
                  const xla::XlaComputation& computation,
                  const xla::cpu::CpuAotCompilationOptions& aot_opts,
                  CompileResult* compile_result) {
  // Retrieves arg and result layouts from the computation.
  // TODO(toddw): Should we let the user choose the major/minor ordering?

  xla::Tuple(&builder, tuple_values);

  TF_ASSERT_OK_AND_ASSIGN(XlaComputation computation, builder.Build());
  EXPECT_EQ(computation.proto().computations_size(), 2);

  TF_ASSERT_OK(CreateMlirModule());
  TF_ASSERT_OK_AND_ASSIGN(TupleOp root_tuple,
                          ImportXlaComputationIntoModule(computation));
  EXPECT_TRUE(root_tuple);

  int num_func_ops = 0;

namespace foo {
namespace bar {

// MyClass represents a computation previously specified in a
// TensorFlow graph, now compiled into executable code. This extends the generic
// XlaCompiledCpuFunction class with statically type-safe arg and result
// methods. Usage example:
//
//   MyClass computation;
//   // ...set args using computation.argN methods
//   CHECK(computation.Run());
//   // ...inspect results using computation.resultN methods

  }

  if (output_to_input_alias.empty()) return absl::OkStatus();

  xla::HloModuleProto* module_proto =
      compilation_result->computation->mutable_proto();
  absl::StatusOr<xla::ProgramShape> program_shape_or_status =
      compilation_result->computation->GetProgramShape();
  TF_RET_CHECK(program_shape_or_status.ok());

  xla::ProgramShape& program_shape = program_shape_or_status.value();

}

// Returns a set of ops that are outside compiled and can be extracted to before
// the TPU computation. These ops are either connected to the inputs of the TPU
// computation or other ops that can be extracted, and have no operands from
// other ops in the TPU computation that cannot be extracted.
llvm::SmallVector<Operation*, 4> FindOutsideCompiledOpsAtHead(

    std::vector<std::vector<xla::Shape>> per_core_arg_shapes;

    tpu::TPUCompileMetadataProto metadata_proto;
    std::vector<TensorShape> arg_shapes;
    if (computation.index() == 0) {
      TF_RETURN_IF_ERROR(tensorflow::tf2xla::internal::ConfigureMetadata(
          std::get<0>(computation).mlir_module, arg_shapes, metadata_proto));
    }

    XlaShapeLayoutHelpers::ShapeDeterminationFns shape_determination_fns;

{{DECLS_FROM_OBJ_FILE}}

{{NS_START}}
// {{CLASS}} represents a computation previously specified in a
// TensorFlow graph, now compiled into executable code. This extends the generic
// XlaCompiledCpuFunction class with statically type-safe arg and result
// methods. Usage example:
//
//   {{CLASS}} computation;
//   // ...set args using computation.argN methods
//   CHECK(computation.Run());