# via
    #   -r ci/official/requirements_updater/requirements.in
    #   h5py
    #   jax
    #   keras-nightly
    #   ml-dtypes
    #   opt-einsum
    #   scipy
    #   tb-nightly
nvidia-cublas-cu12==12.5.3.2 \
    --hash=sha256:4960f3dc5f39699acadf76fa6d94b10a2a00f2956c2c442efa299fb22b0748f3 \
    --hash=sha256:7d0191251180de606023d396b94d66f66470a0ae96d1dbb906c7656ea0f71eda \

    # via
    #   -r ci/official/requirements_updater/requirements.in
    #   h5py
    #   jax
    #   keras-nightly
    #   ml-dtypes
    #   opt-einsum
    #   scipy
    #   tb-nightly
nvidia-cublas-cu12==12.5.3.2 \
    --hash=sha256:4960f3dc5f39699acadf76fa6d94b10a2a00f2956c2c442efa299fb22b0748f3 \
    --hash=sha256:7d0191251180de606023d396b94d66f66470a0ae96d1dbb906c7656ea0f71eda \

    }

    /**
     * Returns the type of this SID indicating the state or type of account.
     * <p>
     * SID types are described in the following table.
     * {@code
     * <table>
     * <tr><th>Type</th><th>Name</th></tr>
     * <tr><td>SID_TYPE_USE_NONE</td><td>0</td></tr>
     * <tr><td>SID_TYPE_USER</td><td>User</td></tr>

이러한 문제를 해결하기 위해 몇 가지 **추가 모델**을 추가할 것입니다. 바로 여기서 SQLModel이 빛을 발하게 됩니다. ✨

### 여러 모델 생성하기

**SQLModel**에서 `table=True`가 설정된 모델 클래스는 **테이블 모델**입니다.

`table=True`가 없는 모델 클래스는 **데이터 모델**로, 이는 실제로 몇 가지 추가 기능이 포함된 Pydantic 모델에 불과합니다. 🤓

SQLModel을 사용하면 **상속**을 통해 모든 경우에 필드를 **중복 선언하지 않아도** 됩니다.

#### `HeroBase` - 기본 클래스

        out.println("<b><a class=\"plain\" href=\".\">normal</a> | <a class=\"plain\" href=\"?fmt=detail\">detailed</a></b>");
        out.println("<p><table border='0' cellspacing='0' cellpadding='0'><tr><td>");

        out.print("<A style=\"width: " + maxLen);
        out.print("; height: 18;\" HREF=\"");
        out.print(path);
        out.println("\"><b>&uarr;</b></a>");

import com.google.common.collect.SetMultimap;
import com.google.common.collect.SortedMapDifference;
import com.google.common.collect.SortedMultiset;
import com.google.common.collect.SortedSetMultimap;
import com.google.common.collect.Table;
import com.google.common.io.ByteSink;
import com.google.common.io.ByteSource;
import com.google.common.io.CharSink;
import com.google.common.io.CharSource;
import com.google.common.primitives.UnsignedInteger;

        return doFindEpg(_epgMap, prop);
    }

    // ===================================================================================
    //                                                                          Table Info
    //                                                                          ==========
    protected final String _tableDbName = "access_token";
    protected final String _tableDispName = "access_token";

    }
  }

  // We use power-of-2 tables, and this is the highest int that's a power of 2
  static final int MAX_TABLE_SIZE = Ints.MAX_POWER_OF_TWO;

  // Represents how tightly we can pack things, as a maximum.
  private static final double DESIRED_LOAD_FACTOR = 0.7;

  // If the set has this many elements, it will "max out" the table size

    # via
    #   -r ci/official/requirements_updater/requirements.in
    #   h5py
    #   jax
    #   keras-nightly
    #   ml-dtypes
    #   opt-einsum
    #   scipy
    #   tb-nightly
nvidia-cublas-cu12==12.5.3.2 \
    --hash=sha256:4960f3dc5f39699acadf76fa6d94b10a2a00f2956c2c442efa299fb22b0748f3 \
    --hash=sha256:7d0191251180de606023d396b94d66f66470a0ae96d1dbb906c7656ea0f71eda \

     *
     *
     * <table>
     * <caption>LM Compatibility Levels</caption>
     * <tr>
     * <td>0 or 1</td>
     * <td>LM and NTLM</td>
     * </tr>
     * <tr>
     * <td>2</td>
     * <td>NTLM only</td>
     * </tr>
     * <tr>
     * <td>3-5</td>
     * <td>NTLMv2 only</td>
     * </tr>
     * </table>
     *
     *