namespace mlir {
namespace quant {

// Applies constant folding recursively if the operation and all of its operands
// are foldable. Returns the constants generated by constant-folding or the
// original operation's outputs if not folded.
SmallVector<Value> ConstantFoldOpIfPossible(Operation* op);

// This pattern tries to constant-fold the quantizable operands of supported
// TF operations.
struct ConstantFoldQuantizableOperands : public RewritePattern {

  // within its purview are mutating in nature.
  void PropagatePotentiallyWrittenWithinUnhandledOp(Operation* op);

  // Given a Region associated with the callee and operands from the
  // corresponding callOp, propagate the potentially written decision to the
  // callOp's operands, if the corresponding region's arguments are potentially
  // written resources.
  void PropagatePotentiallyWrittenUpFromCallee(

}

// Print formats using the default formats for its operands and writes to standard output.
// Spaces are added between operands when neither is a string.
// It returns the number of bytes written and any write error encountered.
func Print(a ...any) (n int, err error) {
	return Fprint(os.Stdout, a...)
}

// Sprint formats using the default formats for its operands and returns the resulting string.

  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) {

  }

  std::unique_ptr<OpQuantSpec> spec = GetUniformOpQuantSpec(op);
  absl::flat_hash_set<int> operands = spec->quantizable_operands;
  int quant_dim = -1;
  if (enable_per_channel_quantization && operands.size() == 1) {
    quant_dim = spec->coeff_op_quant_dim[*(operands.begin())];
  }
  attrs.push_back(rewriter.getNamedAttr("rhs_quantization_axis",

result is the receiver (usually named z in that case; see below); if it
is one of the operands x or y it may be safely overwritten (and its memory
reused).

Arithmetic expressions are typically written as a sequence of individual
method calls, with each call corresponding to an operation. The receiver
denotes the result and the method arguments are the operation's operands.
For instance, given three *Int values a, b and c, the invocation

	c.Add(a, b)

// and have at least one operand, result type can be inferred using the first
// operand's type.

#define INFER_RETURN_TYPE_COMPONENTS_FROM_OPERANDS(Op)                \
  LogicalResult Op::inferReturnTypeComponents(                        \
      MLIRContext* context, std::optional<Location> location,         \
      ValueShapeRange operands, DictionaryAttr attributes,            \

// 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.

#include <vector>

#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project

namespace mlir {
namespace TFL {

// Check if the given op has stateful operands and return their stateful
// operand indices.
bool IsStatefulOp(Operation* op, std::vector<int>* stateful_operand_indices);

}  // namespace TFL
}  // namespace mlir

// 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))) {