  void Compute(OpKernelContext* context) override {
    const Tensor& input = context->input(0);
    const Tensor& filter = context->input(1);
    const Tensor& out_backprop = context->input(2);

    // Determine relevant sizes from input and filters.
    int stride_rows = 0, stride_cols = 0;
    int rate_rows = 0, rate_cols = 0;
    int64 pad_top = 0, pad_left = 0;
    int64 out_rows = 0, out_cols = 0;
    ParseSizes(context, strides_, rates_, padding_, &stride_rows, &stride_cols,
               &rate_rows, &rate_cols, &pad_top, &pad_left, &out_rows,
               &out_cols);
    if (!context->status().ok()) return;

    // Verify that the incoming gradient tensor has the expected size
    // [ batch, out_rows, out_cols, depth ]
    const int batch = input.dim_size(0);
    const int depth = input.dim_size(3);
    OP_REQUIRES(context,
                batch == out_backprop.dim_size(0) &&
                    out_rows == out_backprop.dim_size(1) &&
                    out_cols == out_backprop.dim_size(2) &&
                    depth == out_backprop.dim_size(3),
                errors::InvalidArgument("out_backprop has incompatible size."));

    // The computed filter_backprop has the same dimensions as the filter:
    // [ batch, input_rows, input_cols, depth ]
    Tensor* filter_backprop = nullptr;
    OP_REQUIRES_OK(
        context, context->allocate_output(0, filter.shape(), &filter_backprop));

    // If there is nothing to compute, return.
    if (filter.shape().num_elements() == 0) {
      return;
    }

    functor::DilationBackpropFilter<Device, T>()(
        context->eigen_device<Device>(), input.tensor<T, 4>(),
        filter.tensor<T, 3>(), out_backprop.tensor<T, 4>(), stride_rows,
        stride_cols, rate_rows, rate_cols, pad_top, pad_left,
        filter_backprop->tensor<T, 3>());
  }
The software writes data past the end, or before the beginning, of the intended buffer.