>();
}

OpFoldResult StorageCastOp::fold(FoldAdaptor) {
  // Matches x -> [scast -> scast] -> y, replacing the second scast with the
  // value of x if the casts invert each other.
  auto srcScastOp = getArg().getDefiningOp<StorageCastOp>();
  if (!srcScastOp || srcScastOp.getArg().getType() != getType())
    return OpFoldResult();
  return srcScastOp.getArg();
}

/// The quantization specification should match the expressed type.

// of our files will have access to `OpHasSameStaticShapes` when including files
// generated from table-gen.
def OpHasSameStaticShapesPred : CPred<"OpHasSameStaticShapes($0.getDefiningOp())">;
def OpHasSameStaticShapes : Constraint<OpHasSameStaticShapesPred, "op must have static same input shapes">;
def OpHasNotSameStaticShapes : Constraint<Neg<OpHasSameStaticShapesPred>, "op must have not static same input shapes">;

  for (uint64_t i = 0, e = op->getNumOperands(); i < e; ++i) {
    // Check that the i'th operand is a broadcast.
    auto broadcast = llvm::dyn_cast_or_null<TF::BroadcastToOp>(
        op->getOpOperand(i).get().getDefiningOp());
    if (!broadcast) continue;

    // Check that the operand of the broadcast has fully defined shape.
    auto broadcast_arg_type = mlir::dyn_cast_or_null<RankedTensorType>(

  LogicalResult matchAndRewrite(TFL::DequantizeOp dequant_op,
                                PatternRewriter& rewriter) const override {
    // We only fold i32 -> float pattern.
    auto input = dequant_op.getInput().getDefiningOp();
    if (!input) return failure();

    auto input_dequant = llvm::dyn_cast_or_null<TFL::QConstOp>(input);
    if (!input_dequant) return failure();

  if (shaped_type) {
    return shaped_type.getElementType();
  } else {
    return type;
  }
}

bool NotFromQuantOpOrSameQuantType(mlir::Value val, mlir::TypeAttr qtype_attr) {
  auto val_defn_op = val.getDefiningOp();
  mlir::TFL::QuantizeOp q_op =
      llvm::dyn_cast_or_null<mlir::TFL::QuantizeOp>(val_defn_op);
  if (!q_op) return true;

  // Ignore shape details - we're really only trying to

    shape_op.setOperand(replicate_op.GetReplicaOperandForBlockArgument(
        block_arg, /*replica=*/0));

    return;
  }

  Operation* input_def = input.getDefiningOp();

  // If ShapeOp operand is a ReadVariableOp result where the ReadVariableOp
  // operand is a replicate resource block argument, replace ShapeOp with

// returns the owner of the Block.
Operation* GetOpOfValue(Value value) {
  if (auto block_arg = mlir::dyn_cast<BlockArgument>(value))
    return block_arg.getOwner()->getParentOp();

  return value.getDefiningOp();
}

// Check if the TPUCopyWithDynamicShapeOp is valid.
// 1. The op should be wrapped inside a launch op.
// 2. The wrapped launch op should be placed on CPU.
LogicalResult CheckOpIsValid(Operation* op) {

  if (!converter.isLegal(convert_op.getOperand().getType())) {
    auto other_convert_op = dyn_cast_or_null<OtherConvertOp>(
        convert_op.getOperand().getDefiningOp());
    return other_convert_op &&
           converter.isLegal(other_convert_op.getOperand().getType());
  } else if (!converter.isLegal(convert_op.getResult().getType())) {
    if (!convert_op.getResult().hasOneUse()) {

  for (int i = 0; i != num_return_operands; ++i) {
    auto returned_value = terminator->getOperand(i);
    Type returned_type = returned_value.getType();
    Operation* returned_op = returned_value.getDefiningOp();
    if (returned_op && llvm::isa<DequantizeOp>(returned_op)) {
      auto dequantize_op = llvm::cast<DequantizeOp>(returned_op);
      auto dequantize_input = dequantize_op.getInput();

Operation* GetInputOpWithOneUse(Operation* op, int opr_num) {
  if (opr_num >= op->getNumOperands()) return nullptr;
  auto opr = op->getOperand(opr_num);
  if (llvm::isa<BlockArgument>(opr)) return nullptr;
  auto* res = opr.getDefiningOp();
  if (!res->hasOneUse()) return nullptr;
  return res;
}

// Checks if the given operand of given operation refers to a splat constant
// with given val.
bool HasSplatArg(Operation* op, float val, int opr_num) {