// In order to be clustered operation can require its operands to satisfy
// some constraints (e.g. reduction operation can require reduction dimension
// operand to be a constant value).
enum class ValueConstraint {
  // Operand must have statically known rank.
  kRank = 0,
  // Operand must have statically known shape (all dimensions are known at
  // compile time).
  kShape = 1,

// If an error occurs, x.mode is set to invalid.
func (check *Checker) matchTypes(x, y *operand) {
	// mayConvert reports whether the operands x and y may
	// possibly have matching types after converting one
	// untyped operand to the type of the other.
	// If mayConvert returns true, we try to convert the
	// operands to each other's types, and if that fails
	// we report a conversion failure.

};

// Rewrites the given op to `OpT` op after adding the given operand at the end.
template <typename OpT>
OpT AddOperandAndRewriteAs(Operation* op, Value operand, NamedAttrList attr,
                           OpBuilder* builder) {
  builder->setInsertionPoint(op);
  auto operands = llvm::to_vector<4>(op->getOperands());
  operands.push_back(operand);
  auto new_op = builder->create<OpT>(op->getLoc(), op->getResultTypes(),

// Returns the RankedTensorType for the given operand. TensorFlow constant ops
// may have non-static shape because the shape is not propagated during constant
// folding. If the defining op for the given operand is a constant op, this
// routine uses the constant op's attribute to get the actual shape.
RankedTensorType GetRankedTensorTypeForOperand(Value operand) {
  DenseElementsAttr attr;
  if (matchPattern(operand, m_Constant(&attr))) {

    }
    // op has implict arguments not listed in operands.
    // Fact: if every op's operands are defined in the same block as op,
    //       then no operation has implicit arugments (constant doesn't count).
    for (auto operand : op->getOperands()) {
      if (mlir::dyn_cast_or_null<BlockArgument>(operand)) continue;
      auto operand_op = operand.getDefiningOp();
      if (IsConstant(operand_op)) continue;

1. Operand ordering

In Go asm, the last operand (right) is the target operand, but with PPC64 asm,
the first operand (left) is the target. The order of the remaining operands is
not consistent: in general opcodes with 3 operands that perform math or logical
operations have their operands in reverse order. Opcodes for vector instructions
and those with more than 3 operands usually have operands in the same order except

         matchPattern(op, m_Constant()) || isa<QConstOp>(op);
}

// Pre-order traverse, adding results and BlockArgs to `been_defined` and
// collecting operands not contained within `been_defined`. If we encounter an
// operand that references a Value that has been defined (and added to
// `been_defined`) it is garuanteed that the Value definition is not contained

  if (flatten_tuple) {
    auto operands = llvm::to_vector(values);
    operands.push_back(token);
    return operands;
  }

  auto value = values[0];
  // If value with token already exists, reuse it.
  auto it = rewritten_values.find(value);
  if (it != rewritten_values.end()) return {it->getSecond()};

  auto create_tuple = [&](ArrayRef<Value> operands) {

  rewriter.replaceAllUsesExcept(result, new_value, new_value.getDefiningOp());
}

// Wrap operands in an an unrealized cast to create a cast to buffer any type
// changes to the operand, and apply type converter to operands:
//   V0 = op(operand)
//   ==>
//   V0 = unrealized_cast(operand)
//   V1 = op(V0)
void WrapOperandsInUnrealizedCastAndConvert(Operation *op,

    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,