}
				return ptr;
			};

			const argc = this.argv.length;

			const argvPtrs = [];
			this.argv.forEach((arg) => {
				argvPtrs.push(strPtr(arg));
			});
			argvPtrs.push(0);

			const keys = Object.keys(this.env).sort();
			keys.forEach((key) => {
				argvPtrs.push(strPtr(`${key}=${this.env[key]}`));
			});
			argvPtrs.push(0);

			const argv = offset;
			argvPtrs.forEach((ptr) => {

			<integer>100</integer>
		</dict>
	</dict>
</dict>
</plist>
`

const lldbDriver = `
import sys
import os
import signal

platform, exe, device_exe_or_pid, args = sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4:]

env = []
for k, v in os.environ.items():
	env.append(k + "=" + v)

sys.path.append('/Applications/Xcode.app/Contents/SharedFrameworks/LLDB.framework/Resources/Python')

  }
  return op_reg_data->op_def.is_stateful();
}

void TF_InitMain(const char* usage, int* argc, char*** argv) {
  tensorflow::port::InitMain(usage, argc, argv);
}

int TF_PickUnusedPortOrDie() {
  return tensorflow::internal::PickUnusedPortOrDie();
}

TFE_TensorHandle* TFE_NewTensorHandleFromScalar(TF_DataType data_type,
                                                void* data, size_t len,

}

}  // namespace
}  // namespace tensorflow

GTEST_API_ int main(int argc, char** argv) {
  tensorflow::testing::InstallStacktraceHandler();
  if (!GetTmpDir()) {
    std::cerr << "Could not read GCS_TEST_TMPDIR env";
    return -1;
  }
  ::testing::InitGoogleTest(&argc, argv);
  return RUN_ALL_TESTS();

// Platform specific initialization routine. Very few platforms actually require
// this to be called.
TF_CAPI_EXPORT void TF_InitMain(const char* usage, int* argc, char*** argv);

// Platform-specific implementation to return an unused port. (This should used
// in tests only.)
TF_CAPI_EXPORT int TF_PickUnusedPortOrDie(void);

		return nil, errIncorrectSQLFunctionArgumentType(err)
	}

	// Assemble other arguments
	arg2, arg3 := e.From, e.For
	// Check if the second form of substring is being used
	if e.From == nil {
		arg2, arg3 = e.Arg2, e.Arg3
	}

	// Evaluate the FROM argument
	v2, err := arg2.evalNode(r, tableAlias)
	if err != nil {
		return nil, err
	}
	inferTypeForArithOp(v2)

//		clause.Associations: {"arg1"},
//		"k1":                {"arg2"},
//		"k2.k3":             {"arg3"},
//		"k4.k5.k6":          {"arg4"},
//	})
//	// preloadMap is
//	map[string]map[string][]interface{}{
//		"k0": {},
//		"k7": {
//			"k8": {},
//		},
//		"k1": {},
//		"k2": {
//			"k3": {"arg3"},
//		},
//		"k4": {
//			"k5.k6": {"arg4"},
//		},
//	}

     * @param args
     *            引数
     * @return フォーマットされたメッセージ文字列
     */
    public static LogMessage format(final String messageCode, final Object... args) {
        assertArgumentNotEmpty("messageCode", messageCode);

        final char messageType = messageCode.charAt(0);
        final String message = MessageFormatter.getSimpleMessage(messageCode, args);
        switch (messageType) {

                                                            arg0="${f:h(q)}" arg1="${f:h(allRecordCount)}"
                                                            arg2="${f:h(currentStartRecordNumber)}"
                                                            arg3="${f:h(currentEndRecordNumber)}"/>

  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  auto* arg0 = TF_AddFunctionParameter(graph_ctx, TF_FLOAT, {-1, nullptr}, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  auto* arg1 = TF_AddFunctionParameter(graph_ctx, TF_FLOAT, {-1, nullptr}, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  // Create a first "Add" computing `arg0 + arg1`.
  TF_AbstractTensor* add_output1;
  {