// which is cyclical.
  return success();
}

inline OpFoldResult foldIdempotent(Operation* op) {
  if (op->getNumOperands() == 1) {
    auto* argumentOp = op->getOperand(0).getDefiningOp();
    if (argumentOp && op->getName() == argumentOp->getName()) {
      // Replace the outer operation output with the inner operation.
      return op->getOperand(0);
    }

              extern_value.getDefiningOp())) {
        extern_values.insert(extern_value);
        continue;
      }
      // Add constant at start of region.
      auto const_builder =
          OpBuilder(&it.value()->front(), it.value()->front().begin());
      auto const_value = const_builder.clone(*extern_value.getDefiningOp());

        if (!element_type.isF32()) {
          continue;
        }

        // Skip when there is any already existing CustomAggregatorOp found.
        Operation *defining_op = input.get().getDefiningOp();
        if (dyn_cast_or_null<TF::CustomAggregatorOp>(defining_op)) {
          continue;
        }

        // Skip calibration when the given operand comes from a constant.

       llvm::zip(cluster_terminator->getOperands(), cluster.getResults())) {
    Value cluster_terminator_operand = std::get<0>(result);
    if (cluster_terminator_operand.getDefiningOp() &&
        cluster.getOperation()->isProperAncestor(
            cluster_terminator_operand.getDefiningOp())) {
      new_cluster_results.push_back(cluster_terminator_operand);
      new_cluster_result_types.push_back(cluster_terminator_operand.getType());

  // We need to make sure both splits & split dim are constants.
  auto splits = splitv_op.getSizeSplits().getDefiningOp();
  mlir::DenseIntElementsAttr splits_attr;
  if (!splits || !matchPattern(splits, m_Constant(&splits_attr)))
    return failure();

  auto split_dim = splitv_op.getSplitDim().getDefiningOp();
  mlir::ElementsAttr split_dim_attr;
  if (!split_dim || !matchPattern(split_dim, m_Constant(&split_dim_attr)))

  // Broadcast bias value if unmatched with output shape.
  auto bcast_op = TryCast<BroadcastInDimOp>(bias_op.getDefiningOp(),
                                            /*name=*/"broadcast_in_dim_op");

  if (failed(bcast_op)) {
    bcast_op = TryCast<DynamicBroadcastInDimOp>(
        bias_op.getDefiningOp(),
        /*name=*/"dynamic_broadcast_in_dim_op");
  }
  // Update the bias type for both static and dynamic broadcasts.

mlir::Operation* GetXlaShardingFromOperand(Value value) {
  Value value_to_visit = value;
  if (auto read_var = value_to_visit.getDefiningOp<mlir::TF::ReadVariableOp>())
    value_to_visit = read_var.getResource();

  if (auto partitioned_input =
          value_to_visit.getDefiningOp<mlir::TF::TPUPartitionedInputV2Op>()) {
    return NullUnlessSharded(partitioned_input);
  }

  return nullptr;
}

    // Only values defined by IslandOp were inserted in the worklist.
    IslandOp current_island = cast<IslandOp>(control_barrier.getDefiningOp());

    for (auto control_input : current_island.getControlInputs()) {
      ops_connected_to_fetch->insert(control_input.getDefiningOp());
      if (IsNoOpControlBarrier(control_input))
        control_barrier_worklist.push(control_input);
    }
    current_island.erase();

          return write.getValue().getType();
        } else if (auto split =
                       llvm::dyn_cast<TF::TensorArraySplitV3Op>(user)) {
          if (!split.getLengths().getDefiningOp() ||
              !llvm::isa<TF::ConstOp>(split.getLengths().getDefiningOp())) {
            return std::nullopt;
          }
          RankedTensorType t;
          int64_t count;

      if (matchPattern(quantize_operand, m_Constant(&attr))) {
        // Const-> QuantizeOp pattern will be handled separately.
        return failure();
      }
      if (Operation* quantizing_op = quantize_operand.getDefiningOp()) {
        quantizing_ops.push_back(quantizing_op);
      }
    }

    tensorflow::DataType inference_type =
        quant_params_.quant_spec.inference_type;
    bool weight_only_quantization =