  void Compute(OpKernelContext* context) override {
    const Tensor& indices = context->input(0);
    const Tensor& values = context->input(1);
    const Tensor& shape = context->input(2);
    const Tensor& weights = context->input(3);
    bool use_weights = weights.NumElements() > 0;

    OP_REQUIRES(context, TensorShapeUtils::IsMatrix(indices.shape()),
                errors::InvalidArgument(
                    "Input indices must be a 2-dimensional tensor. Got: ",
                    indices.shape().DebugString()));
    OP_REQUIRES(context, TensorShapeUtils::IsVector(values.shape()),
                errors::InvalidArgument("Input values must be a vector. Got: ",
                                        values.shape().DebugString()));
    OP_REQUIRES(context, TensorShapeUtils::IsVector(shape.shape()),
                errors::InvalidArgument("Input shape must be a vector. Got: ",
                                        shape.shape().DebugString()));
    OP_REQUIRES(context,
                values.shape().dim_size(0) == indices.shape().dim_size(0),
                errors::InvalidArgument(
                    "Number of values must match first dimension of indices.",
                    "Got ", values.shape().dim_size(0),
                    " values, indices shape: ", indices.shape().DebugString()));
    OP_REQUIRES(
        context, shape.shape().dim_size(0) == indices.shape().dim_size(1),
        errors::InvalidArgument(
            "Number of dimensions must match second dimension of indices.",
            "Got ", shape.shape().dim_size(0),
            " dimensions, indices shape: ", indices.shape().DebugString()));
    OP_REQUIRES(context, shape.NumElements() > 0,
                errors::InvalidArgument(
                    "The shape argument requires at least one element."));
    // Validate indices: each index must be valid for the corresponding
    // dimension. This could be possibly done better.
    const auto indices_values = indices.matrix<int64_t>();
    const auto shape_vector = shape.vec<int64_t>();
    int num_values = values.NumElements();  // same as first dim of indices
    int rank = indices.shape().dim_size(1);
    for (int i = 0; i < num_values; ++i) {
      for (int j = 0; j < rank; ++j) {
        OP_REQUIRES(
            context,
            indices_values(i, j) >= 0 && indices_values(i, j) < shape_vector(j),
            errors::InvalidArgument(
                "Invalid index value at ", i, ": dimension ", j, " has value ",
                indices_values(i, j), " which is not in [0, ", shape_vector(j),
                ") (as given by dense shape ", shape.DebugString()));
      }
    }

    if (use_weights) {
      OP_REQUIRES(
          context, weights.shape() == values.shape(),
          errors::InvalidArgument(
              "Weights and values must have the same shape. Weight shape: ",
              weights.shape().DebugString(),
              "; values shape: ", values.shape().DebugString()));
    }

    bool is_1d = shape.NumElements() == 1;
    int num_batches = is_1d ? 1 : shape_vector(0);
    OP_REQUIRES(
        context, 0 < num_batches && num_batches < kMaxBatches,
        errors::InvalidArgument("Cannot allocate ", num_batches,
                                " batches, is the dense shape too wide?"));

    const auto values_values = values.flat<T>();
    const auto weight_values = weights.flat<W>();

    auto per_batch_counts = BatchedMap<W>(num_batches);

    T max_value = 0;

    for (int idx = 0; idx < num_values; ++idx) {
      int batch = is_1d ? 0 : indices_values(idx, 0);
      if (batch >= num_batches) {
        OP_REQUIRES(context, batch < num_batches,
                    errors::InvalidArgument(
                        "Indices value along the first dimension must be ",
                        "lower than the first index of the shape.", "Got ",
                        batch, " as batch and ", num_batches,
                        " as the first dimension of the shape."));
      }
      const auto& value = values_values(idx);
      if (value >= 0 && (maxlength_ <= 0 || value < maxlength_)) {
        if (binary_output_) {
          per_batch_counts[batch][value] = 1;
        } else if (use_weights) {
          per_batch_counts[batch][value] += weight_values(idx);
        } else {
          per_batch_counts[batch][value]++;
        }
        if (value > max_value) {
          max_value = value;
        }
      }
    }

    int num_output_values = GetOutputSize(max_value, maxlength_, minlength_);
    OP_REQUIRES_OK(context, OutputSparse<W>(per_batch_counts, num_output_values,
                                            is_1d, context));
  }
Unknown