/*disabled_checks=*/empty_array_attr);
  xla_call_module_op->setAttr(TF::kStablehloEntryFunctionAttrName,
                              SymbolRefAttr::get(stablehlo_func_op));
  // Set jax.uses_shape_polymorphism=true to enable shape refinement at runtime.
  // This is needed for native serialization version >= 8.
  xla_call_module_op->setAttr(
      kStablehloModuleAttrsAttrName,

                                PatternRewriter& rewriter) const override {
    auto input_op = op.getInput().getDefiningOp();
    if (auto q = llvm::dyn_cast_or_null<QuantizeOp>(input_op)) {
      if (!q->getAttr(mlir::quant::kVolatileOpAttrName)) return failure();

      if (remove_volatile_ops_type == kPreserveInputsAndOutputs) {
        // Don't remove leading and trailing QDQ for PTQ workflow, so the io

  if (!op->hasAttr(TF::kTpuReplicateAttr)) {
    op->emitOpError("TF2XLA TPU bridge input check: " + errormsg() +
                    "missing _tpu_replicate attr");
    return false;
  }
  auto opattr = op->getAttr(TF::kTpuReplicateAttr);
  if (opattr != attr) {
    op->emitOpError("TF2XLA TPU bridge input check: " + errormsg() +
                    "invalid _tpu_replicate attr.")

		{"GetsockoptInt", Func, 0},
		{"GetsockoptUcred", Func, 1},
		{"Gettid", Func, 0},
		{"Gettimeofday", Func, 0},
		{"Getuid", Func, 0},
		{"Getwd", Func, 0},
		{"Getxattr", Func, 1},
		{"HANDLE_FLAG_INHERIT", Const, 0},
		{"HKEY_CLASSES_ROOT", Const, 0},
		{"HKEY_CURRENT_CONFIG", Const, 0},
		{"HKEY_CURRENT_USER", Const, 0},
		{"HKEY_DYN_DATA", Const, 0},

    if (check_device) {
      // TODO(lyandy): Wrap resource ops in tf_device.launch.
      if (auto* resource_op = resource.getDefiningOp()) {
        auto resource_attr = resource_op->getAttr(kDeviceAttr);
        // Check device matching for the node defining the resource.
        if (!IsResourceMergeable(resource_attr, device_attr)) continue;
      } else {

    BlockArgument block_arg) {
  assert(block_arg.getOwner() == &GetBody());

  unsigned arg_number = block_arg.getArgNumber();
  unsigned num_replicated_args = GetNumReplicatedBlockArguments();
  int32_t num_replicas = getNAttr().getInt();
  MutableArrayRef<OpOperand> operands = getOperation()->getOpOperands();

  // All replicated arguments are before packed arguments so return replicated

      updated_input_shapes_attr.emplace_back(input_shapes_attr[i]);
    }

    // Replaces the attribute with the updated "#tf_type.shape" array.
    // Builder builder(func_op.getContext());
    func_op->setAttr(
        kTfInputShapesAttr,
        ArrayAttr::get(func_op.getContext(), updated_input_shapes_attr));
  }
}

// Validates func ops. Returns `failure` if the function is invalid.

|callable|chr|classmethod|cmp|coerce|compile|complex|delattr|dict|dir|divmod|enumerate|eval|execfile|file|filter|float|format|frozenset|getattr|globals|hasattr|hash|help|hex|id|input|int|intern|isinstance|issubclass|iter|len|list|locals|long|map|max|memoryview|min|next|object|oct|open|ord|pow|property|range|raw_input|reduce|reload|repr|reversed|round|set|setattr|slice|sorted|staticmethod|str|sum|super|tuple|type|unichr|unicode|vars|xrange|zip)\b/,boolean:/\b(?:True|False|None)\b/,number:/(?:\b(?...

  builder.insert(metadata_op.clone());
  for (Operation& op : func.getOps()) {
    if (!IsTPUOp(&op)) continue;
    op.setAttr(TF::kReplicationInfoAttr, new_group);
  }
  TF::TPUCompilationResultOp new_result = compilation_op.clone();
  new_result->setAttr(kTpuCompilationStatus, new_group);
  builder.insert(new_result);
}

template <typename OpType>
LogicalResult FindAndExcludeOp(func::FuncOp func,

                                                TF_Status* status) {
  ::tensorflow::TensorShape shape;
  auto* cc_ctx = reinterpret_cast<::tensorflow::OpKernelConstruction*>(ctx);
  ::tensorflow::Status s = cc_ctx->GetAttr(attr_name, &shape);
  ::tensorflow::Set_TF_Status_from_Status(status, s);
  size_t rank = static_cast<size_t>(shape.dims());

  if (!status->status.ok()) return;

  if (num_dims != rank) {