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});

    }
  }

  /**
   * 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.
   *

    Network<Integer, String> transpose = transpose(directedGraph);
    assertThat(transpose).isEqualTo(expectedTranspose);
    assertThat(transpose(transpose)).isSameInstanceAs(directedGraph);
    AbstractNetworkTest.validateNetwork(transpose);

    assertThat(transpose.edgesConnecting(N1, N2)).isEmpty();
    assertThat(transpose.edgeConnecting(N1, N2)).isEmpty();
    assertThat(transpose.edgeConnectingOrNull(N1, N2)).isNull();

    }
  }

  /**
   * 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.
   *

    Table<String, Integer, Character> transpose = transpose(original);
    original.put(1, "foo", 'a');
    assertEquals((Character) 'a', transpose.get("foo", 1));
  }

  public void testPutTransposeModifiesOriginal() {
    Table<Integer, String, Character> original = HashBasedTable.create();
    Table<String, Integer, Character> transpose = transpose(original);
    transpose.put("foo", 1, 'a');

    public int inDegree(N node) {
      return delegate().outDegree(node); // transpose
    }

    @Override
    public int outDegree(N node) {
      return delegate().inDegree(node); // transpose
    }

    @Override
    public boolean hasEdgeConnecting(N nodeU, N nodeV) {
      return delegate().hasEdgeConnecting(nodeV, nodeU); // transpose
    }

    @Override

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))

for i := range e1 { e1[i] = samplePolyCBD(rnd, N) N++ } e2 := samplePolyCBD(rnd, N) u := make([]ringElement, k1024) // NTT⁻¹(AT ◦ r) + e1 for i := range u { var uHat nttElement for j := range r { // Note that i and j are inverted, as we need the transposed of A. uHat = polyAdd(uHat, nttMul(ex.a[j*k1024+i], r[j])) } u[i] = polyAdd(e1[i], inverseNTT(uHat)) } μ := ringDecodeAndDecompr(m) var vNTT nttElement // t⊺ ◦ r for i := range ex.t { vNTT = polyAdd(vNTT, nttMul(ex.t[i], r[i])) } v := polyAdd(po...