if (!mlir::cast<ShapedType>(conv_op.getRhs().getType()).hasStaticShape()) {
      return false;
    }
    if (!mlir::cast<ShapedType>(conv_op.getLhs().getType()).hasStaticShape() &&
        !mlir::cast<ShapedType>(conv_op.getType()).hasStaticShape()) {
      auto dnums = conv_op.getDimensionNumbers();
      auto lhs_type = mlir::cast<ShapedType>(conv_op.getLhs().getType());

    Value constant_val = add_op ? add_op.getRhs() : sub_op.getRhs();
    if (!matchPattern(constant_val, m_Constant(&added_value))) return failure();

    // Check the add op is applied to begin.
    mlir::TypedValue<::mlir::TensorType> begin_tensor =
        strided_slice_op.getBegin();
    mlir::TypedValue<::mlir::TensorType> add_source_tensor =
        add_op ? add_op.getLhs() : sub_op.getLhs();
    if (begin_tensor != add_source_tensor) {

  }

  const auto input_type = op.getLhs().getType().cast<TensorType>();
  if (input_type.getRank() != 4) {
    LLVM_DEBUG(llvm::dbgs() << "Only 2D convolution op is supported. "
                               "Expected input rank of 4. Got: "
                            << input_type.getRank() << ".\n");
    return failure();
  }

  const auto filter_type = op.getRhs().getType().cast<TensorType>();

OpFoldResult MaximumOp::fold(FoldAdaptor adaptor) {
  auto lhs_type = getLhs().getType();
  auto rhs_type = getRhs().getType();
  // Only constant fold for float tensors of the same type is implemented.
  if (lhs_type != rhs_type || !IsFloatShapedType(lhs_type)) return nullptr;

  auto lhs = adaptor.getLhs().dyn_cast_or_null<DenseElementsAttr>();
  auto rhs = adaptor.getRhs().dyn_cast_or_null<DenseElementsAttr>();
  if (lhs && lhs.isSplat()) {

// "third_party/tensorflow/compiler/mlir/tensorflow/ir/tf_ops_n_z.cc" is
// resolved
LogicalResult PrecheckForXlaConvV2Op(XlaConvV2Op op) {
  auto input_tensor = op.getLhs();
  auto kernel_tensor = op.getRhs();
  auto window_strides = op.getWindowStrides();
  auto padding = op.getPadding();
  auto lhs_dilation = op.getLhsDilation();
  auto rhs_dilation = op.getRhsDilation();

    RegionRange regions,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  XlaBroadcastHelperOpAdaptor op(operands.getValues(), attributes);
  Value lhs = op.getLhs();
  Value rhs = op.getRhs();
  auto set_unranked_results = [&]() {
    inferredReturnShapes.emplace_back(getElementTypeOrSelf(lhs));
    inferredReturnShapes.emplace_back(getElementTypeOrSelf(rhs));
    return success();
  };

                        reg.getFrom().attribute(artifactType, "jar")
                        reg.getTo().attribute(artifactType, "intermediate")
                    }

                    registerTransform(FileSizer) { reg ->
                        reg.getFrom().attribute(artifactType, "intermediate")
                        reg.getTo().attribute(artifactType, "final")
                    }
                }