return operand_ty == result_ty && identity_ty.hasStaticShape() &&
           result_ty.hasStaticShape() &&
           OpTrait::util::staticallyKnownBroadcastable(operand_ty.getShape(),
                                                       identity_ty.getShape());
  };

  // Check that we have a constant operand on one side (candidate for identity).
  const bool is_commutative =

    // Reshape input image to add a new spatial dimension.
    auto image_type = mlir::cast<ShapedType>(conv_op.getLhs().getType());
    SmallVector<int64_t, 4> image_2d_shape(image_type.getShape().begin(),
                                           image_type.getShape().end());
    image_2d_shape.push_back(1);
    auto image_2d_type =
        RankedTensorType::get(image_2d_shape, image_type.getElementType());
    auto loc = conv_op.getLoc();

            if ( t != null ) {
                if ( log.isDebugEnabled() ) {
                    log.debug("Tree is " + t);
                }

                if ( Objects.equals(loc.getShare(), t.getShare()) ) {
                    try ( SmbSessionImpl session = t.getSession() ) {
                        targetDomain = session.getTargetDomain();
                        if ( !session.isFailed() ) {

    auto new_shape_const_attr =
        DenseElementsAttr::get(shape_spec_type, new_shape.getShape());
    rewriter.setInsertionPointAfter(weight_op);
    auto new_shape_const = rewriter.create<TF::ConstOp>(
        weight_op->getLoc(), shape_spec_type, new_shape_const_attr);
    auto reshape_op = rewriter.create<TF::ReshapeOp>(
        weight_op->getLoc(), new_shape, weight_op->getResult(0),
        new_shape_const);

    const int num_dimensions = input_tensor.getShapedType().getRank();
    assert(perm_tensor.getType().getNumElements() == num_dimensions);

    ArrayRef<int64_t> input_shape = input_tensor.getShapedType().getShape();
    auto output_type = mlir::cast<ShapedType>(op.getOutput().getType());

    SmallVector<int32_t, 4> perm;
    SmallVector<int64_t, 4> output_shape;
    for (int i = 0; i < num_dimensions; ++i) {

      "outputs": [1],
      "operators": [
        {
          "inputs": [ 0 ],
          "outputs": [ 1 ],
          "builtin_options_type": "ReshapeOptions",
          "builtin_options": {
            "new_shape": [ 2, 2 ]
          }
        }
      ]
    }
  ],
  "buffers": []

  Type dtype = getElementTypeOrSelf(type);
  if (RankedTensorType ty = type.dyn_cast<RankedTensorType>()) {
    llvm::SmallVector<int64_t, 4> shape = {dim};
    shape.append(ty.getShape().begin(), ty.getShape().end());
    return tensorflow::GetTypeFromTFTensorShape(shape, dtype);
  }
  return type;
}

Type GetTensorTypeForTensorList(Type element_type, TF::VariantType handle_dtype,

            return false;
        }
        DfsReferralData other = (DfsReferralData) obj;

        return Objects.equals(getServer(), other.getServer()) && Objects.equals(getShare(), other.getShare())
                && Objects.equals(getPath(), other.getPath()) && Objects.equals(getPathConsumed(), other.getPathConsumed());
    }


    /**
     * @param ref
     * @param reqPath

def HasEqualShape : Constraint<CPred<
  "$0.getType().cast<ShapedType>().hasRank() && "
  "$1.getType().cast<ShapedType>().hasRank() && "
  "$0.getType().cast<ShapedType>().getShape() == $1.getType().cast<ShapedType>().getShape()">,
  "Checks if the shapes of tensors are same.">;

// Make the 1D value $0 broadcastable with the shape of $1.
def MakeOneDimValueBroadcastable : NativeCodeCall<

      auto scatter_dims_to_operand_dims =
          scatter_dimension_numbers.getScatterDimsToOperandDims();

      if (IsIotaAttr(inserted_window_dims, indices_type.getShape().back()) &&
          IsIotaAttr(scatter_dims_to_operand_dims,
                     indices_type.getShape().back())) {
        rewriter.replaceOpWithNewOp<TfOp>(scatter_op,
                                          scatter_op.getResult(0).getType(),