  void Compute(OpKernelContext* ctx) override {
    const Tensor* inputs;
    const Tensor* labels_indices;
    const Tensor* labels_values;
    const Tensor* seq_len;
    OP_REQUIRES_OK(ctx, ctx->input("inputs", &inputs));
    OP_REQUIRES_OK(ctx, ctx->input("labels_indices", &labels_indices));
    OP_REQUIRES_OK(ctx, ctx->input("labels_values", &labels_values));
    OP_REQUIRES_OK(ctx, ctx->input("sequence_length", &seq_len));

    OP_REQUIRES(ctx, inputs->shape().dims() == 3,
                errors::InvalidArgument("inputs is not a 3-Tensor"));
    OP_REQUIRES(ctx, TensorShapeUtils::IsVector(seq_len->shape()),
                errors::InvalidArgument("sequence_length is not a vector"));
    OP_REQUIRES(ctx, TensorShapeUtils::IsMatrix(labels_indices->shape()),
                errors::InvalidArgument("labels_indices is not a matrix"));
    OP_REQUIRES(ctx, TensorShapeUtils::IsVector(labels_values->shape()),
                errors::InvalidArgument("labels_values is not a vector"));

    const TensorShape& inputs_shape = inputs->shape();
    const int64 max_time = inputs_shape.dim_size(0);
    const int64 batch_size = inputs_shape.dim_size(1);
    const int64 num_classes_raw = inputs_shape.dim_size(2);
    OP_REQUIRES(
        ctx, FastBoundsCheck(num_classes_raw, std::numeric_limits<int>::max()),
        errors::InvalidArgument("num_classes cannot exceed max int"));
    const int num_classes = static_cast<const int>(num_classes_raw);

    OP_REQUIRES(
        ctx, batch_size == seq_len->dim_size(0),
        errors::InvalidArgument("len(sequence_length) != batch_size.  ",
                                "len(sequence_length):  ", seq_len->dim_size(0),
                                " batch_size: ", batch_size));
    auto seq_len_t = seq_len->vec<int32>();

    OP_REQUIRES(ctx, labels_indices->dim_size(0) == labels_values->dim_size(0),
                errors::InvalidArgument(
                    "labels_indices and labels_values must contain the "
                    "same number of rows, but saw shapes: ",
                    labels_indices->shape().DebugString(), " vs. ",
                    labels_values->shape().DebugString()));

    OP_REQUIRES(ctx, batch_size != 0,
                errors::InvalidArgument("batch_size must not be 0"));

    // Figure out the maximum label length to use as sparse tensor dimension.
    auto labels_indices_t = labels_indices->matrix<int64>();
    int64 max_label_len = 0;
    for (int i = 0; i < labels_indices->dim_size(0); i++) {
      max_label_len = std::max(max_label_len, labels_indices_t(i, 1) + 1);
    }

    TensorShape labels_shape({batch_size, max_label_len});
    std::vector<int64> order{0, 1};
    sparse::SparseTensor labels_sp;
    OP_REQUIRES_OK(
        ctx, sparse::SparseTensor::Create(*labels_indices, *labels_values,
                                          labels_shape, order, &labels_sp));

    Status labels_sp_valid = labels_sp.IndicesValid();
    OP_REQUIRES(ctx, labels_sp_valid.ok(),
                errors::InvalidArgument("label SparseTensor is not valid: ",
                                        labels_sp_valid.error_message()));

    typename ctc::CTCLossCalculator<T>::LabelSequences labels_t(batch_size);
    for (const auto& g : labels_sp.group({0})) {  // iterate by batch
      const int64 batch_indices = g.group()[0];
      OP_REQUIRES(ctx, FastBoundsCheck(batch_indices, batch_size),
                  errors::InvalidArgument("labels batch index must be between ",
                                          0, " and ", batch_size,
                                          " but saw: ", batch_indices));

      auto values = g.values<int32>();
      std::vector<int>* b_values = &labels_t[batch_indices];
      b_values->resize(values.size());
      for (int i = 0; i < values.size(); ++i) (*b_values)[i] = values(i);
    }

    OP_REQUIRES(ctx, static_cast<size_t>(batch_size) == labels_t.size(),
                errors::InvalidArgument("len(labels) != batch_size.  ",
                                        "len(labels):  ", labels_t.size(),
                                        " batch_size: ", batch_size));

    for (int64 b = 0; b < batch_size; ++b) {
      OP_REQUIRES(
          ctx, seq_len_t(b) <= max_time,
          errors::InvalidArgument("sequence_length(", b, ") <= ", max_time));
    }

    Tensor* loss = nullptr;
    OP_REQUIRES_OK(ctx, ctx->allocate_output("loss", seq_len->shape(), &loss));
    auto loss_t = loss->vec<T>();

    Tensor* gradient;
    OP_REQUIRES_OK(ctx,
                   ctx->allocate_output("gradient", inputs_shape, &gradient));
    auto gradient_t = gradient->tensor<T, 3>();
    auto inputs_t = inputs->tensor<T, 3>();
    std::vector<OutputMap> gradient_list_t;
    std::vector<InputMap> input_list_t;

    for (std::size_t t = 0; t < max_time; ++t) {
      input_list_t.emplace_back(inputs_t.data() + t * batch_size * num_classes,
                                batch_size, num_classes);
      gradient_list_t.emplace_back(
          gradient_t.data() + t * batch_size * num_classes, batch_size,
          num_classes);
    }

    gradient_t.setZero();

    // Assumption: the blank index is num_classes - 1
    ctc::CTCLossCalculator<T> ctc_loss_calculator(num_classes - 1, 0);
    DeviceBase::CpuWorkerThreads workers =
        *ctx->device()->tensorflow_cpu_worker_threads();
    OP_REQUIRES_OK(ctx, ctc_loss_calculator.CalculateLoss(
                            seq_len_t, labels_t, input_list_t,
                            preprocess_collapse_repeated_, ctc_merge_repeated_,
                            ignore_longer_outputs_than_inputs_, &loss_t,
                            &gradient_list_t, &workers));
  }