absl::Span<AbstractTensorHandle*> outputs) {
  return ops::MatMul(ctx, inputs[0], inputs[1], &outputs[0],
                     /*transpose_a=*/false,
                     /*transpose_b=*/false, "MatMul");
}

absl::Status MulModel(AbstractContext* ctx,
                      absl::Span<AbstractTensorHandle* const> inputs,

                       TF_Operation* r, const char* name,
                       bool transpose_a = false, bool transpose_b = false) {
    TF_OperationDescription* desc = TF_NewOperation(graph, "MatMul", name);
    if (transpose_a) {
      TF_SetAttrBool(desc, "transpose_a", 1);
    }
    if (transpose_b) {
      TF_SetAttrBool(desc, "transpose_b", 1);
    }
    TF_AddInput(desc, {l, 0});
    TF_AddInput(desc, {r, 0});

    Table<String, Integer, Character> transpose = transpose(original);
    original.put(1, "foo", 'a');
    assertSame(original.columnKeySet(), transpose.rowKeySet());
    assertSame(original.rowKeySet(), transpose.columnKeySet());
    assertSame(original.columnMap(), transpose.rowMap());
    assertSame(original.rowMap(), transpose.columnMap());
    assertSame(original.values(), transpose.values());

    }
  }

  /**
   * Creates a transposed view of a given table that flips its row and column keys. In other words,
   * calling {@code get(columnKey, rowKey)} on the generated table always returns the same value as
   * calling {@code get(rowKey, columnKey)} on the original table. Updating the original table
   * changes the contents of the transposed table and vice versa.
   *

 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package com.google.common.collect;

import static com.google.common.collect.Tables.transpose;

import com.google.common.annotations.GwtCompatible;
import com.google.common.collect.TableCollectionTest.ColumnTests;

@GwtCompatible
@ElementTypesAreNonnullByDefault

 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package com.google.common.collect;

import static com.google.common.collect.Tables.transpose;

import com.google.common.annotations.GwtCompatible;
import com.google.common.collect.TableCollectionTest.ColumnTests;

@GwtCompatible
@ElementTypesAreNonnullByDefault

import static com.google.common.collect.Tables.immutableCell;
import static com.google.common.collect.Tables.transformValues;
import static com.google.common.collect.Tables.transpose;
import static com.google.common.collect.Tables.unmodifiableRowSortedTable;
import static com.google.common.collect.Tables.unmodifiableTable;
import static java.util.Collections.sort;

import static com.google.common.collect.Tables.immutableCell;
import static com.google.common.collect.Tables.transformValues;
import static com.google.common.collect.Tables.transpose;
import static com.google.common.collect.Tables.unmodifiableRowSortedTable;
import static com.google.common.collect.Tables.unmodifiableTable;
import static java.util.Collections.sort;

*   Fixes a `CHECK` failures in `UnbatchGradOp` ([CVE-2022-35952](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-35952))
*   Fixes a segfault TFLite converter on per-channel quantized transposed convolutions ([CVE-2022-36027](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-36027))
*   Fixes a `CHECK` failures in `AvgPool3DGrad` ([CVE-2022-35959](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-35959))