// be categorized as cusom ops despite each of them may require different
    // behaviors. In that case, these ops can be marked in the custom map and
    // treated separately in this pass.

    auto custom_op = llvm::dyn_cast_or_null<CustomOp>(op);
    if (!custom_op) return false;

    // Custom op which is marked in the custom op map is quantizable.

      "_xla", "_oc", "cluster", name_attrs, "cluster_rewritten", "host_graph",
      host_compute_core, &fld, &shape_inference_graphs,
      &has_outside_compilation));

  // Check host graph is not created.
  EXPECT_EQ(fld.Find("host_graph"), nullptr);
}

TEST_F(ExtractOutsideCompilationForFunctionTest, OutsideCompilationInIf) {
  // Build the XLA computation func.
  // "const0" (bool)
  // "const1" (int32)

        result_hash, tensorflow::Fingerprint64(concrete_function_serialized));
  }
  // TODO(b/241294832): Complete canonicalization of `object_graph_def.nodes`.
  return result_hash;
}

// Creates a FingerprintDef proto from a SavedModel and the checkpoint meta file
// (.index) in `export_dir`.
absl::StatusOr<FingerprintDef> CreateFingerprintDefPb(
    absl::string_view export_dir, std::string pb_file) {

  nodes_[inst] = node;
  return absl::OkStatus();
}

bool IsEntryFunctionArg(BlockArgument arg) {
  return arg.getParentRegion()->getParentOfType<FuncOp>().getName() == "main";
}

// Creates argument nodes from Block argument. If a name is supplied, that
// name will be used instead of generating a unique name.
Status Exporter::AddArgumentNode(BlockArgument arg, unsigned index,

  nodes_[inst] = node;
  return absl::OkStatus();
}

bool IsEntryFunctionArg(BlockArgument arg) {
  return arg.getParentRegion()->getParentOfType<FuncOp>().getName() == "main";
}

// Creates argument nodes from Block argument. If a name is supplied, that
// name will be used instead of generating a unique name.
Status Exporter::AddArgumentNode(BlockArgument arg, unsigned index,

            /*quant_dim=*/-1, num_bits, narrow_range, is_signed);
        builder.setInsertionPoint(block, insertion_point);
        auto q_op = builder.create<quantfork::QuantizeCastOp>(
            loc, params.getValue(), arg);
        auto dq_op = builder.create<quantfork::DequantizeCastOp>(
            loc, input_type, q_op.getResult());
        arg.replaceAllUsesWith(dq_op.getResult());
        q_op.setOperand(arg);
      }

        // Keep track of IDs of resources that are allocated by ops with
        // `UniqueResourceAllocation` trait, this can be utilized for while-loop
        // parallelization (every iteration creates a new unique resource).
        if (op->hasTrait<OpTrait::TF::UniqueResourceAllocation>()) {
          unique_resource_allocation_ids_.insert(resource_handle.id);
        }
      }

  EXPECT_TRUE(xla::LiteralTestUtil::Equal(
      *literal1, xla::LiteralUtil::CreateR2<int32_t>({{1, 2, 3}})));

  std::shared_ptr<xla::Literal> literal2 = *exec_args[1]->ToLiteralSync();
  EXPECT_TRUE(xla::LiteralTestUtil::Equal(
      *literal2, xla::LiteralUtil::CreateR2<int32_t>({{4, 5, 6}})));
}

}  // namespace
}  // namespace tensorflow

#endif  // PLATFORM_GOOGLE

    const llvm::StringMap<Attribute>& derived_attrs) const {
  // Create the new op
  Location loc = call_op.getLoc();
  rewriter.setInsertionPointAfter(call_op);
  std::string tf_op_name = GetTFOpName(call_op.getCallee());
  OperationState new_state(loc, tf_op_name, inputs, output_types, attr_list);
  Operation* new_op = rewriter.create(new_state);
  if (materialize_derived_attrs_) {

  auto launch = builder->create<tf_device::LaunchOp>(
      before_op->getLoc(), builder->getStringAttr(host_device),
      launch_result_types);
  launch.getBody().push_back(launch_block);

  builder->setInsertionPointToEnd(&launch.GetBody());
  builder->create<tf_device::ReturnOp>(before_op->getLoc(), launch_results);

  return launch;
}

// Creates a new_launch after `before_op` with `ops_to_move` from