}

    auto fused_loc = rewriter.getFusedLoc(locations);

    // The fused contraction has the same operands as the original contraction
    // with `bias` from the BiasAddOp appended.
    SmallVector<Value, 4> operands(contraction.operand_begin(),
                                   contraction.operand_end());
    operands.push_back(bias_add.getBias());

    // The fused contraction has the same attributes as the original

// immutable because they are from quantization-aware training.
//
// The algorithm traverses each op and sets the quantization parameters of its
// operands and results, according to its quantization specification, and then
// adds the operands and results to the worklist. If there are any conflicts
// (for example, there are quantization parameters propagated from the previous

    }
    // 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;

	keep := not(choice(underflow)) | choice(always)
	x.assign(keep, t)
}

// Sub computes x = x - y mod m.
//
// The length of both operands must be the same as the modulus. Both operands
// must already be reduced modulo m.
func (x *Nat) Sub(y *Nat, m *Modulus) *Nat {
	underflow := x.sub(y)
	// If the subtraction underflowed, add m.
	t := NewNat().set(x)
	t.add(m.nat)
	x.assign(choice(underflow), t)

  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,

    // 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);
      }
    });

	cgofunc:   "cgo function",
}

// An operand represents an intermediate value during type checking.
// Operands have an (addressing) mode, the expression evaluating to
// the operand, the operand's type, a value for constants, and an id
// for built-in functions.
// The zero value of operand is a ready to use invalid operand.
type operand struct {
	mode operandMode
	expr ast.Expr
	typ  Type
	val  constant.Value
	id   builtinId
}

      Value operand = op->getOperand(i);
      if (Operation* inst = operand.getDefiningOp()) {
        // If the operand comes from a `quantfork::DequantizeCastOp`, we use
        // the quantized input of this `quantfork::DequantizeCastOp` to set the
        // state.
        if (auto dq = dyn_cast<quantfork::DequantizeCastOp>(inst)) {
          operand = dq.getArg();
        }
      }

struct OpQuantSpec {
  // Maps the operand index of a bias input to its quantization specifications,
  // including the non-bias operand indexes and the method retrieving
  // quantization parameters from list of parameters of the non-bias operands.
  // This map is empty if the op doesn't have a bias operand.
  BiasParamsMap biases_params;

  // that was extracted..

  // Find the input edges to form the set of operands to the new function call.
  llvm::SetVector<Value> inputs;
  for (Operation* op : ops) {
    for (Value operand : op->getOperands()) {
      Operation* defining_op = operand.getDefiningOp();
      if (!ops.contains(defining_op)) inputs.insert(operand);
    }
  }
  // Find the output edges to form the set of resutls of the new function call.