Given the following code containing errors, provide Common Weakness Enumeration (CWE) identifiers and corresponding messages in JSON format. Each identified weakness should have a unique entry in the JSON array.
    Code:   void Compute(OpKernelContext* ctx) final {
    // Extract inputs and validate shapes and types.
    const CSRSparseMatrix* input_matrix;
    OP_REQUIRES_OK(ctx, ExtractVariantFromInput(ctx, 0, &input_matrix));
    const Tensor& input_permutation_indices = ctx->input(1);

    int64 num_rows;
    int batch_size;
    ValidateInputs(ctx, *input_matrix, input_permutation_indices, &batch_size,
                   &num_rows);

    // Allocate batch pointers.
    Tensor batch_ptr(cpu_allocator(), DT_INT32, TensorShape({batch_size + 1}));
    auto batch_ptr_vec = batch_ptr.vec<int32>();
    batch_ptr_vec(0) = 0;

    // Temporary vector of Eigen SparseMatrices to store the Sparse Cholesky
    // factors.
    // Note: we use column-compressed (CSC) SparseMatrix because SimplicialLLT
    // returns the factors in column major format. Since our input should be
    // symmetric, column major and row major is identical in storage. We just
    // have to switch to reading the upper triangular part of the input, which
    // corresponds to the lower triangular part in row major format.
    std::vector<SparseMatrix> sparse_cholesky_factors(batch_size);

    // TODO(anudhyan): Tune the cost per unit based on benchmarks.
    const double nnz_per_row =
        (input_matrix->total_nnz() / batch_size) / num_rows;
    const int64 sparse_cholesky_cost_per_batch =
        nnz_per_row * nnz_per_row * num_rows;
    // Perform sparse Cholesky factorization of each batch in parallel.
    auto worker_threads = *(ctx->device()->tensorflow_cpu_worker_threads());
    std::atomic<int64> invalid_input_index(-1);
    Shard(worker_threads.num_threads, worker_threads.workers, batch_size,
          sparse_cholesky_cost_per_batch,
          [&](int64 batch_begin, int64 batch_end) {
            for (int64 batch_index = batch_begin; batch_index < batch_end;
                 ++batch_index) {
              // Define an Eigen SparseMatrix Map to operate on the
              // CSRSparseMatrix component without copying the data.
              Eigen::Map<const SparseMatrix> sparse_matrix(
                  num_rows, num_rows, input_matrix->nnz(batch_index),
                  input_matrix->row_pointers_vec(batch_index).data(),
                  input_matrix->col_indices_vec(batch_index).data(),
                  input_matrix->values_vec<T>(batch_index).data());

              Eigen::SimplicialLLT<SparseMatrix, Eigen::Upper,
                                   Eigen::NaturalOrdering<int>>
                  solver;
              auto permutation_indices_flat =
                  input_permutation_indices.flat<int32>().data();

              // Invert the fill-in reducing ordering and apply it to the input
              // sparse matrix.
              Eigen::Map<
                  Eigen::PermutationMatrix<Eigen::Dynamic, Eigen::Dynamic, int>>
                  permutation(permutation_indices_flat + batch_index * num_rows,
                              num_rows);
              auto permutation_inverse = permutation.inverse();

              SparseMatrix permuted_sparse_matrix;
              permuted_sparse_matrix.template selfadjointView<Eigen::Upper>() =
                  sparse_matrix.template selfadjointView<Eigen::Upper>()
                      .twistedBy(permutation_inverse);

              // Compute the Cholesky decomposition.
              solver.compute(permuted_sparse_matrix);
              if (solver.info() != Eigen::Success) {
                invalid_input_index = batch_index;
                return;
              }

              // Get the upper triangular factor, which would end up in the
              // lower triangular part of the output CSRSparseMatrix when
              // interpreted in row major format.
              sparse_cholesky_factors[batch_index] =
                  std::move(solver.matrixU());
              // For now, batch_ptr contains the number of nonzeros in each
              // batch.
              batch_ptr_vec(batch_index + 1) =
                  sparse_cholesky_factors[batch_index].nonZeros();
            }
          });

    // Check for invalid input.
    OP_REQUIRES(
        ctx, invalid_input_index == -1,
        errors::InvalidArgument(
            "Sparse Cholesky factorization failed for batch index ",
            invalid_input_index.load(), ". The input might not be valid."));

    // Compute a cumulative sum to obtain the batch pointers.
    std::partial_sum(batch_ptr_vec.data(),
                     batch_ptr_vec.data() + batch_size + 1,
                     batch_ptr_vec.data());

    // Allocate output Tensors.
    const int64 total_nnz = batch_ptr_vec(batch_size);
    Tensor output_row_ptr(cpu_allocator(), DT_INT32,
                          TensorShape({(num_rows + 1) * batch_size}));
    Tensor output_col_ind(cpu_allocator(), DT_INT32, TensorShape({total_nnz}));
    Tensor output_values(cpu_allocator(), DataTypeToEnum<T>::value,
                         TensorShape({total_nnz}));
    auto output_row_ptr_ptr = output_row_ptr.flat<int32>().data();
    auto output_col_ind_ptr = output_col_ind.flat<int32>().data();
    auto output_values_ptr = output_values.flat<T>().data();

    // Copy the output matrices from each batch into the CSRSparseMatrix
    // Tensors.
    // TODO(b/129906419): Factor out the copy from Eigen SparseMatrix to
    // CSRSparseMatrix into common utils. This is also used in
    // SparseMatrixSparseMatMul.
    Shard(worker_threads.num_threads, worker_threads.workers, batch_size,
          (3 * total_nnz) / batch_size /* cost per unit */,
          [&](int64 batch_begin, int64 batch_end) {
            for (int64 batch_index = batch_begin; batch_index < batch_end;
                 ++batch_index) {
              const SparseMatrix& cholesky_factor =
                  sparse_cholesky_factors[batch_index];
              const int64 nnz = cholesky_factor.nonZeros();

              std::copy(cholesky_factor.outerIndexPtr(),
                        cholesky_factor.outerIndexPtr() + num_rows + 1,
                        output_row_ptr_ptr + batch_index * (num_rows + 1));
              std::copy(cholesky_factor.innerIndexPtr(),
                        cholesky_factor.innerIndexPtr() + nnz,
                        output_col_ind_ptr + batch_ptr_vec(batch_index));
              std::copy(cholesky_factor.valuePtr(),
                        cholesky_factor.valuePtr() + nnz,
                        output_values_ptr + batch_ptr_vec(batch_index));
            }
          });

    // Create the CSRSparseMatrix instance from its component Tensors and
    // prepare the Variant output Tensor.
    CSRSparseMatrix output_csr_matrix;
    OP_REQUIRES_OK(
        ctx,
        CSRSparseMatrix::CreateCSRSparseMatrix(
            DataTypeToEnum<T>::value, input_matrix->dense_shape(), batch_ptr,
            output_row_ptr, output_col_ind, output_values, &output_csr_matrix));
    Tensor* output_csr_matrix_tensor;
    AllocatorAttributes cpu_alloc;
    cpu_alloc.set_on_host(true);
    OP_REQUIRES_OK(
        ctx, ctx->allocate_output(0, TensorShape({}), &output_csr_matrix_tensor,
                                  cpu_alloc));
    output_csr_matrix_tensor->scalar<Variant>()() =
        std::move(output_csr_matrix);
  } ; Sample JSON Format Confermily Expected output: "examples" => "cwe": "xxx"; "message": "xxx"
    
 'cwe': ['CWE-476'], 'message': 'Remove `OP_REQUIRES` call from helper function.

Since `OP_REQUIRES` macro expands to a `return;` (among other), calling it in a helper function only ends the helper function's execution earlier, but the kernel will still run from start to end. Thus, all the expected validations are actually broken/useless as the code ploughs through the next crash anyway.

PiperOrigin-RevId: 369524386
Change-Id: I54f6cf9328445675ccc392e661b04336b229c9da'