DenseIntElementsAttr iota_const_attr;
  if (!matchPattern(iota, m_Constant(&iota_const_attr))) return false;

  auto iota_type = iota_const_attr.getType();
  auto iota_shape = iota_type.getShape();
  auto reduce_dim = (*dimensions.value_begin<APInt>()).getSExtValue();
  if (reduce_dim < 0) reduce_dim += iota_type.getRank();

  auto index =

    /**
     * @return the service
     */
    public String getService () {
        return this.service;
    }


    /**
     * @return the share
     */
    public String getShare () {
        return this.share;
    }


    /**
     * @return whether this is a DFS share
     */
    public boolean isDfs () {
        return this.inDfs;
    }


    /**

      // required by the new converter, from the reshape op's option.
      auto new_shape = op.builtin_options.AsReshapeOptions()->new_shape;
      auto shape_type = tensorflow::GetTypeFromTFTensorShape(
          {static_cast<int64_t>(new_shape.size())}, builder.getIntegerType(32));

      mlir::SmallVector<mlir::Attribute, 4> shape;
      for (auto s : new_shape) {
        shape.push_back(

  if (!ranked_type)
    return context_op->emitOpError()
           << "A map_outside_compilation op's input and output types must be "
              "ranked tensors.";
  ArrayRef<int64_t> in_shape = ranked_type.getShape();
  if (in_shape.empty() || in_shape[0] < 0) {
    return context_op->emitOpError()
           << "A map_outside_compilation op's input and output shapes must "

        }

        shape_inference::ShapeHandle handle;
        TF_RETURN_IF_ERROR(
            context->MakeShapeFromPartialTensorShape(arg_shape.shape, &handle));
        TF_RETURN_IF_ERROR(shape_refiner->SetShape(n, 0, handle));
      }
    }

    // Sometimes we have VariableShape nodes in while loop (after Enter nodes).
    // They won't be constant-folded because TensorFlow constant folding does

  auto ref = op.input(0);
  auto indices = op.input(1);
  Shape::Attrs shape_attrs;
  shape_attrs.out_type_ = indices.type();
  auto ref_shape = Shape(scope, ref, shape_attrs);
  grad_outputs->push_back(ScatterNd(scope, indices, grad_inputs[0], ref_shape));
  grad_outputs->push_back(NoGradient());
  return scope.status();
}
REGISTER_GRADIENT_OP("GatherNd", GatherNdGrad);

// a TensorFlow graph.
//
// Abstractly, if we have a cluster of this form:
//
//   x0 = arg0
//   x1 = arg1
//     ...
//   shape = f(x0, x1, ...)
//   result = Reshape(input=<something>, new_shape=shape)
//
// then pulling `f` out of the cluster may reduce the number of compilations and
// will never increase the number of compilations.
//

  auto ranked_ty = mlir::dyn_cast_or_null<mlir::RankedTensorType>(ty);
  if (!ranked_ty) return {};

  int64_t rank = ranked_ty.getRank();
  llvm::SmallVector<int64_t, 4> dims = llvm::to_vector<4>(ranked_ty.getShape());
  auto encoding = mlir::dyn_cast_or_null<mlir::mhlo::TypeExtensionsAttr>(
      ranked_ty.getEncoding());
  if (encoding && !encoding.getBounds().empty()) {
    for (int64_t dim = 0; dim < rank; ++dim) {

      return op_->emitRemark()
             << "skipping legalization due to unsupported type "
             << operand.getType();
    }

    auto shape_or = expr.GetShape();
    if (!shape_or.ok()) {
      return op_->emitRemark()
             << "failed to get shape for expression. " << expr.HumanString();
    }

    tensors.emplace_back(

        aqtype.getZeroPoints(), new_out_quant_dim, /*narrow_range=*/true);
    auto new_tensor_type = RankedTensorType::getChecked(
        transpose_op.getLoc(), transpose_op.getType().getShape(), new_qtype);
    rewriter.setInsertionPointAfter(transpose_op);
    auto new_q_op = rewriter.create<quantfork::QuantizeCastOp>(
        transpose_op.getLoc(), new_tensor_type, q_op.getArg());