{6, 15, 24, 6, 15, 24, 6, 15, 24}};

  for (int i{}; i < 4; ++i) {
    bool transpose_a = transpose_a_vals[i];
    bool transpose_b = transpose_b_vals[i];
    Model MatMulModel =
        [transpose_a, transpose_b](
            AbstractContext* ctx,
            absl::Span<AbstractTensorHandle* const> inputs,

                                /*transpose_a = */ false,
                                /*transpose_b = */ true, name_grad_A.c_str()));

      TF_RETURN_IF_ERROR(MatMul(ctx, A.get(), upstream_grad, &matmul_B_output,
                                /*transpose_a = */ true,
                                /*transpose_b = */ false, name_grad_B.c_str()));
    } else if (!t_a && t_b) {

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

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

                       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.
   *

    Table<String, Integer, Character> transpose = Tables.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());

    expectedTranspose.addEdge(N4, N3, E34);

    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.edgeConnectingOrNull(N1, N2)).isNull();

    Table<String, Integer, Character> transpose = Tables.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());

    expectedTranspose.addEdge(N4, N3, E34);

    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).isPresent()).isFalse();

    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