}

// Given Value input_size, and known numbers filter_sz, dilation_rate, stride,
// calculate padding_low and padding_high for SAME padding.
void GetSamePaddingValues(OpBuilder &builder, Location loc, Value input_size,
                          int64_t filter_sz, int64_t dilation_rate,
                          int64_t stride, Value &padding_low,
                          Value &padding_high) {

                        Value filter_size, int64_t dilation_rate,
                        int64_t stride, tensorflow::Padding padding_type,
                        Type shape_scalar_type, Value *padding_low,
                        Value *padding_high) const {
    // Stride must be > 0
    if (stride <= 0) return false;
    // Dilation rate must be >= 1
    if (dilation_rate < 1) return false;

    Location loc = op.getLoc();

static LogicalResult ComputeConvWindowedOutputSize(
    int64_t input_size, int64_t filter_size, int64_t dilation_rate,
    int64_t stride, tensorflow::Padding padding, int64_t* output_size) {
  int64_t pad_low;
  int64_t pad_high;

  tensorflow::Status status = tensorflow::GetWindowedOutputSizeVerbose(
      input_size, filter_size, dilation_rate, stride, padding, output_size,
      &pad_low, &pad_high);

    // following condition must hold true for padding to be `VALID`:
    // output_spatial_shape[i] = ceil((input_spatial_shape[i] -
    // (spatial_filter_shape[i]-1) * dilation_rate[i]) / strides[i])
    auto get_output_dim_for_valid_padding =
        [](int64_t input_dim, int64_t dilation_dim, int64_t kernel_dim,
           int64_t stride_dim) -> int64_t {