// Simple case, single device cluster.

module {
  // CHECK-LABEL: func @singlecluster
  // CHECK-SAME: (%[[ARG_0:[a-z0-9]*]]: tensor<?xi32>)
  func.func @singlecluster(%arg0: tensor<?xi32>) -> tensor<?xi32> {
    // CHECK: %[[A_OUTPUT:[0-9]*]] = "tf.A"(%[[ARG_0]])
    %2 = "tf.A"(%arg0) : (tensor<?xi32>) -> tensor<?xi32>

    // CHECK: %[[TPU0_OUTPUT:[0-9]*]] = "tf_device.launch"

     %arg60, %arg61, %arg62, %arg63, %arg64, %arg65, %arg66, %arg67, %arg68, %arg69, %arg70, %arg71, %arg72, %arg73, %arg74,
     %arg75, %arg76, %arg77, %arg78, %arg79, %arg80, %arg81, %arg82, %arg83, %arg84, %arg85, %arg86, %arg87, %arg88, %arg89,
     %arg90, %arg91, %arg92, %arg93, %arg94, %arg95, %arg96, %arg97, %arg98, %arg99, %arg100, %arg101, %arg102, %arg103, %arg104,
     %arg105, %arg106, %arg107, %arg108, %arg109, %arg110, %arg111, %arg112

                      dst_index);
    }
    return absl::OkStatus();
  }

  auto input_arg = mlir::cast<BlockArgument>(src);
  auto input_node_it = args_.find(input_arg);
  TF_RET_CHECK(input_node_it != args_.end())
      << "Use of BlockArgument encounted before def!";
  // For argument, there is only one result output, so the index is always 0.

                      dst_index);
    }
    return absl::OkStatus();
  }

  auto input_arg = mlir::cast<BlockArgument>(src);
  auto input_node_it = args_.find(input_arg);
  TF_RET_CHECK(input_node_it != args_.end())
      << "Use of BlockArgument encounted before def!";
  // For argument, there is only one result output, so the index is always 0.

    return
  }

// CHECK: func.func @init_func_restore_op
// Check that an argument ("__tf_file_prefix") is created.
// CHECK-SAME: %[[ARG_0:.*]]: tensor<!tf_type.string> {tf_saved_model.index_path = ["__tf_file_prefix"]}

// Original `AssignVariableOp(VarHandleOp, Const)` pattern persists.

import org.codelibs.fess.timer.SystemMonitorTarget;
import org.codelibs.fess.util.ComponentUtil;
import org.codelibs.fess.util.SystemUtil;
import org.kohsuke.args4j.CmdLineException;
import org.kohsuke.args4j.CmdLineParser;
import org.kohsuke.args4j.Option;
import org.lastaflute.di.core.external.GenericExternalContext;
import org.lastaflute.di.core.external.GenericExternalContextComponentDefRegister;

  func.func @serving_default(%arg_0: tensor<5xf32> {tf_saved_model.index_path = ["input"]}) -> (tensor<5xf32> {tf_saved_model.index_path = ["output"]})
  attributes {tf.entry_function = {control_outputs = "", inputs = "inputs:0", outputs = "output:0"}, tf_saved_model.exported_names = ["serving_default"]} {
    return %arg_0 : tensor<5xf32>
  }
// CHECK: @serving_default(%[[ARG_0:.*]]: tensor<5xf32> {{.*}})

  func.return %4 : tensor<!tf_type.variant>
}
// CHECK-LABEL: __inference_Dataset_flat_map_lambda_190
func.func private @__inference_Dataset_flat_map_lambda_190(%arg0: tensor<i64> {tf._user_specified_name = "args_0"}) -> tensor<!tf_type.variant> attributes {tf._original_func_name = "__inference_Dataset_flat_map_lambda_19", tf._tf_data_function = true, tf.signature.is_stateful} {

    }
    tf_executor.fetch
  }
  func.return
}
// CHECK-LABEL: empty_island_multiple_data_results
// CHECK-SAME: (%[[ARG_0:.*]]: tensor<*xf32>, %[[ARG_1:.*]]: tensor<*xi32>)
// CHECK: %[[IDENTITY_N:.*]]:2 = "tf.IdentityN"
// CHECK-SAME: (%[[ARG_0]], %[[ARG_1]])
// CHECK: tf_executor.yield %[[IDENTITY_N]]#0, %[[IDENTITY_N]]#1

tensor<2x1xf32>, %arg6 as %arg15: tensor<*x!tf_type.resource<tensor<7x7x3x64xf32>>>, %arg8 as %arg16: tensor<*x!tf_type.resource<tensor<1001xf32>>>, %arg7 as %arg17: tensor<*x!tf_type.resource<tensor<64x1001xf32>>>, %arg9 as %arg18: tensor<*x!tf_type.resource<tensor<f32>>>, %arg10 as %arg19: tensor<*x!tf_type.resource<tensor<f32>>>, %arg11 as %arg20: tensor<*x!tf_type.resource<tensor<f32>>>, %arg12 as %arg21: tensor<*x!tf_type.resource<tensor<f32>>>) {_mirrored_variable_indices = [2, 3, 4, 5, 6, 7, 8],...