}
    // 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;

OpFoldResult TFR::EqualOp::fold(FoldAdaptor adaptor) {
  auto operands = adaptor.getOperands();
  assert(operands.size() == 2 && "equal op has two operands");
  auto ctx = getContext();
  if (operands[0] == operands[1]) return BoolAttr::get(ctx, true);
  return BoolAttr::get(ctx, false);
}

OpFoldResult ConstOp::fold(FoldAdaptor adaptor) {
  auto operands = adaptor.getOperands();
  (void)operands;

// CHECK: return %[[identity]] : tensor<1x1x2xf32>

// Tests that an AddV2 op accepting two bf16 operands is transformed into
// an AddV2 op that accepts two fp32 operands.
func.func @cast_bf16_add_v2_to_fp32(%arg0: tensor<2xbf16>, %arg1: tensor<2xbf16>) -> tensor<2xf32> {
  %0 = "tf.AddV2"(%arg0, %arg1) : (tensor<2xbf16>, tensor<2xbf16>) -> tensor<2xbf16>

	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)

    $operands_with_static_shape are indices of operands that are tagged with a maximum static shape.
    $producer_name is a string describing the name of the framework that added support for running this portion of the model on TPUs.

    Example:
      %rendezvous_key_base, %result = tf_mlrt_tpu.compile_and_execute (%operands) constant_operand_indices = [1, 3] metadata = "metadata..." mlir_module = "mlir_module..."

  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);
      }
    });

The `tf_executor.graph` operation does not accept any operands, inputs are
implicitly captured by the region, representing the feeds to the graph.

The region attached to `tf_executor.graph` is terminated by a
`tf_executor.fetch` operation. The non-control operands of the terminator
correspond to the result values (or fetches) of the `tf_executor.graph`
operation. The behavior is undefined if any of the operands of the
`tf_executor.fetch` is dead.

    $operands_with_static_shape are indices of operands that are tagged with a maximum static shape.
    $producer_name is a string describing the name of the framework that added support for running this portion of the model on TPUs.

    Example:
      %rendezvous_key_base, %result = tf_mlrt_tpu.compile_and_execute (%operands) constant_operand_indices = [1, 3] metadata = "metadata..." mlir_module = "mlir_module..."

    // Forward island fetches (tf_executor.yield operands) to island op result
    // uses.
    for (auto result :
         llvm::zip(island_op.getOutputs(), island_op.GetYield().getFetches()))
      std::get<0>(result).replaceAllUsesWith(std::get<1>(result));
  }

  // Forward graph fetches (tf_executor.fetch operands) to graph op result uses.
  for (auto result :