}
  }
  // Verify axisStats (optional) attribute.
  if (getAxisStats()) {
    if (!getAxis()) return emitOpError("axis must be specified for axisStats");

    auto shape = tensorArg.getShape();
    auto argSliceSize =
        std::accumulate(std::next(shape.begin(), *getAxis()), shape.end(), 1,
                        std::multiplies<int64_t>());

    }
  }
  // Verify axisStats (optional) attribute.
  if (getAxisStats()) {
    if (!getAxis()) return emitOpError("axis must be specified for axisStats");

    auto shape = tensorArg.getShape();
    auto argSliceSize =
        std::accumulate(std::next(shape.begin(), *getAxis()), shape.end(), 1,
                        std::multiplies<int64_t>());

  // Figure out output shapes.
  SmallVector<int64_t, 4> concat_out_shape;
  SmallVector<int64_t, 4> pack_out_shape;

  const int64_t rank = input_type.getRank();
  int64_t pack_axis = pack_op.getAxis();
  size_t count = pack_inputs.size();
  if (pack_axis < 0) {
    pack_axis += rank;
  }

  // Concat out shape.
  for (int i = 0; i < rank; ++i) {
    int64_t dim_size = input_type.getDimSize(i);

    for (auto m : fqOp.getMax())
      max.push_back(cast<FloatAttr>(m).getValueAsDouble());

    return fakeQuantAttrsToType(fqOp.getLoc(), fqOp.getNumBits(),
                                fqOp.getAxis(), min, max, fqOp.getNarrowRange(),
                                expressedType, fqOp.getIsSigned());
  }
};

void ConvertSimulatedQuantPass::runOnOperation() {
  bool hadFailure = false;

  auto values = tf_concat_op.getValues();
  auto output_type = tf_concat_op.getOutput().getType();
  // Extract axis attribute from constant axis tensor
  ElementsAttr axis;
  if (!matchPattern(tf_concat_op.getAxis(), m_Constant(&axis)))
    return failure();
  IntegerAttr axis_int = ExtractSingleElementAsInteger(axis);

  // "axis" operand could be a i64 tensor. Resolve it here.
  IntegerAttr axis_i32;

              nullptr>
static LogicalResult Verify(OpT op) {
  if (!IsOfRankOrUnranked(op.getAxis(), 0))
    return op.emitOpError("requires scalar axis operand");

  DenseIntElementsAttr axis_attr;
  if (matchPattern(op.getAxis(), m_Constant(&axis_attr))) {
    auto input_ty = mlir::dyn_cast<RankedTensorType>(op.getX().getType());
    if (input_ty) {

        loc, DenseElementsAttr::get(tensorflow::GetTypeFromTFTensorShape(
                                        {}, rewriter.getIntegerType(64)),
                                    op.getAxis()));
    int64_t axis = op.getAxis();

    Type prev_input_ty, inferred_ty;
    SmallVector<Value, 4> expanded_inputs;
    expanded_inputs.reserve(op.getN());
    for (Value input : op.getValues()) {

    int32_t axis_value = op.getAxis();
    if (axis_value < 0) axis_value += input_type.getRank() + 1;
    if (axis_value < 0 || axis_value >= input_type.getRank() + 1)
      return op.emitOpError()
             << "op attribute 'axis' should be in range [-rank - 1, rank + 1), "
             << "got rank = " << input_type.getRank()
             << ", and axis = " << op.getAxis();
  }

// OneHotOp
//===----------------------------------------------------------------------===//

LogicalResult OneHotOp::verify() {
  OneHotOp op = *this;
  int64_t axis = op.getAxis();

  auto indices_ty = op.getIndices().getType().dyn_cast<RankedTensorType>();
  if (indices_ty &&
      !(axis == -1 || (axis >= 0 && axis <= indices_ty.getShape().size()))) {
    return op.emitOpError()

    if (!shift_ty || shift_ty.getRank() != 0) {
      return rewriter.notifyMatchFailure(
          op, "require the type of shift to be 0D tensor");
    }

    APInt val;
    if (!matchPattern(op.getAxis(), m_ConstantInt(&val))) {
      return rewriter.notifyMatchFailure(op, "require axis to be constant");
    }
    int axis = val.getSExtValue();