the same symbol scope as the callee. The operands and result types of the
    call must match the specified function type. The callee is encoded as a
    symbol reference attribute named "callee".

    Example:

    ```mlir
    %2 = tfr.call @my_add(%0, %1) : (tfr.tensor, f32) -> tfr.tensor_list
    ```

    Note that the operands of the `call` operation can only be with tfr.tensor,

  Value GetNumElements(TF::TensorListReserveOp op, ValueRange operands,
                       PatternRewriter *rewriter) const override {
    Value scalar_zero = CreateI32SplatConst(op.getLoc(), rewriter, {}, 0);
    Type shape_dtype = getElementTypeOrSelf(op.getElementShape().getType());
    Value num_elements = operands[1];
    IntegerAttr attr;
    if (matchPattern(num_elements, m_Constant(&attr))) {

    // Collect operands of cluster op that are generated within the cluster.
    // These values should be returned by the cluster.
    cluster_op.walk([&](Operation* op) {
      for (Value operand : op->getOperands()) {
        Operation* operand_op = GetOpOfValue(operand);
        if (operand_op->getParentRegion() == cluster_region)
          cluster_results_set.insert(operand);
      }
    });

      // For simplicity, we don't consolidate these ops when all the
      // non-canonicalizable operands are adjacent.
      new_values.push_back(
          rewriter
              .create<fallback_async::CoreRTTensorHandleToFallbackTensorOp>(
                  op.getLoc(), rewriter.getType<fallback::TFTensorType>(),
                  operand, op->getAttrOfType<mlir::StringAttr>("device"))

    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
  // VariableShapeOp and use the associated first replica operand as its
  // operand.
  auto read_var_op = llvm::dyn_cast<TF::ReadVariableOp>(input_def);
  if (!read_var_op) return;

	var s uint
	_ = 1.0 /* ERROR "shifted operand 1.0 (type float64) must be integer" */ <<s == 1
	_ = 1.0 /* ERROR "shifted operand 1.0 (type float64) must be integer" */ <<s == 1.0
	_ = 1 /* ERROR "shifted operand 1 (type float64) must be integer" */ <<s == 1.0
	_ = 1 /* ERROR "shifted operand 1 (type float64) must be integer" */ <<s + 1.0 == 1
	_ = 1 /* ERROR "shifted operand 1 (type float64) must be integer" */ <<s + 1.1 == 1

    llvm::SmallVector<Value, 4> operands(addn_op.getInputs().begin(),
                                         addn_op.getInputs().end());

    int64_t n = operands.size();
    // Keep doing tree-based reduction when there are more than one operand.
    while (n > 1) {
      for (int64_t i = 0; i < n; i += 2) {
        // Add two adjacent operands if applicable.
        operands[i / 2] =

    }

    // If any of the operands is a resource type, then we break
    // and mark the module as not valid for TFLite legalization.
    // Note: this might disable native variables in more than needed cases.
    // TODO(b/189370197): Enhance variable analysis.
    for (auto operand : op->getOperands()) {
      if (mlir::isa<TF::ResourceType>(
              getElementTypeOrSelf(operand.getType()))) {
        legalize_to_tfl = false;

    // The map contains all the quantization parameters which are required to
    // satisfy the same operands and results constraint. The keys of this map
    // are the values from `operand_states_` and `result_state_`.
    std::unordered_map<int, RequantizeState> rescale_states_;

    // Maps of indexes to the propagation state vector from the ops operands,
    // results and arguments.
    llvm::DenseMap<OpValue, int> operand_states_;

//
// If the format specifier includes a %w verb with an error operand,
// the returned error will implement an Unwrap method returning the operand.
// If there is more than one %w verb, the returned error will implement an
// Unwrap method returning a []error containing all the %w operands in the
// order they appear in the arguments.
// It is invalid to supply the %w verb with an operand that does not implement