// Due to this, if the padding values in the input are equal, they will become
  // the same constant operator and the following check (which compares memory
  // addresses) works.
  if (pad_op.getPaddingValue() != parent_pad.getPaddingValue()) {
    return rewriter.notifyMatchFailure(
        pad_op, "parent and child pad have different padding values");
  }

  // NOTE: Because negative paddings are allowed, we assert that if

  const int dilation_h = mlir::cast<IntegerAttr>(dilations[1]).getInt();
  const int dilation_w = mlir::cast<IntegerAttr>(dilations[2]).getInt();
  return rewriter.getI64ArrayAttr({dilation_h, dilation_w});
}

Attribute GetPaddingValue(PatternRewriter& rewriter,
                          llvm::StringMap<Attribute>& identifier_to_attr) {
  llvm::StringRef padding =
      mlir::dyn_cast<StringAttr>(identifier_to_attr["padding"]).getValue();

    return false;
  if (ExtractSingleElementAsInteger(num_cols).getInt() != -1) return false;

  // Verify padding_value is a tensor with all 0s.
  mlir::Value padding_value = tf_matrix_diag_v2_or_v3_op.getPaddingValue();
  mlir::Type element_type =
      mlir::cast<ShapedType>(padding_value.getType()).getElementType();
  if (mlir::isa<FloatType>(element_type)) {
    DenseFPElementsAttr padding_attr;