return llvm::formatv("requires attribute '{0}'", attribute).str();
}

// Populates a TPUCompileMetadataProto with StepMarkerLocation from a
// `tf_device::ClusterFuncOp`.
LogicalResult SetMetadataProtoStepMarkerLocation(
    tf_device::ClusterFuncOp op,
    tensorflow::tpu::TPUCompileMetadataProto* metadata) {
  auto step_marker_location =
      op->getAttrOfType<StringAttr>(kStepMarkerLocationAttr);

  SmallVector<tf_device::ClusterFuncOp, 4> cluster_funcs;
  getOperation().walk([&](tf_device::ClusterFuncOp cluster_func) {
    cluster_funcs.push_back(cluster_func);
  });

  OpBuilder builder(&getContext());
  // Add resource reads for resource writes from TPU cluster where for such
  // resources the TPU cluster does not read from.
  for (tf_device::ClusterFuncOp cluster_func : cluster_funcs) {

  llvm::SmallVector<mlir::tf_device::ClusterFuncOp, 4> cluster_func_ops;
  module->walk([&](mlir::tf_device::ClusterFuncOp cluster_func) {
    cluster_func_ops.push_back(cluster_func);
  });
  EXPECT_EQ(cluster_func_ops.size(), 1);

  EXPECT_TRUE(mlir::succeeded(tensorflow::EraseClusterFuncs(cluster_func_ops)));

  llvm::SmallVector<mlir::tf_device::ClusterFuncOp, 4> new_cluster_func_ops;

    int num_cores_per_replica, mlir::tf_device::ClusterFuncOp cluster_func,
    mlir::OpBuilder* builder,
    llvm::SmallVectorImpl<llvm::SmallVector<mlir::Value, 4>>* input_list);

// Extracts a list of OpSharding that represent output sharding configuration of
// `tf_device.cluster`.
mlir::LogicalResult ParseAndValidateOutputSharding(
    int num_cores_per_replica, mlir::tf_device::ClusterFuncOp cluster_func,

                                           callee.getBody().getArgumentTypes(),
                                           callee.getResultTypes()));
}

void RewriteCall(tf_device::ClusterFuncOp cluster_func_op, SymbolTable &symtab,
                 OpBuilder &builder) {
  llvm::SmallVector<Value> non_resource_args, resource_args;
  bool has_resources = false, in_order = true;

    assert(aliasing_attr.getInt() == alias_info.output_index);
  }
}

void MarkInputOutputAliasesPass::runOnOperation() {
  SmallVector<tf_device::ClusterFuncOp, 4> cluster_funcs;
  ModuleOp module = getOperation();
  module.walk([&](tf_device::ClusterFuncOp cluster_func) {
    // Map resource values to pair of input-output indices.
    llvm::DenseMap<Value, AliasInfo> resource_alias_info_map;

}

void TPUSpaceToDepthPass::runOnOperation() {
  std::optional<tf_device::ClusterFuncOp> cluster_func;
  // Space to depth only supports training loop.
  auto func_result = getOperation().walk([&](tf_device::ClusterFuncOp cluster) {
    cluster_func = cluster;
    return WalkResult::interrupt();
  });

  // Return if there is no tf_device::ClusterFuncOp in training loop.
  if (!func_result.wasInterrupted() || !cluster_func.has_value()) {

    return WalkResult::advance();
  });
  if (result_init.wasInterrupted()) return signalPassFailure();
  llvm::SmallVector<tf_device::ClusterFuncOp> to_be_erased;
  OpBuilder builder(&getContext());
  auto result = getOperation().walk([&](tf_device::ClusterFuncOp op) {
    if (failed(TF::HasValidCompilationAndReplicationAttributes(*op)))
      return WalkResult::interrupt();
    // Skip non-tpu device cluster_func.

// XlaSharding op.
void IdentifyXlaShardingForComputationInputs(
    const llvm::SmallVector<std::string>& logical_device_vec,
    bool infer_from_computation, mlir::tf_device::ClusterFuncOp cluster_func,
    mlir::func::FuncOp func, Builder* builder,
    OptionalOpShardingVector& sharding_for_args) {
  // Look up function definition from module.
  Block& function_block = func.front();

    v = op->getOperand(0);
  }
  return v;
}

void AnnotateParameterReplicationPass::runOnOperation() {
  ModuleOp m = getOperation();
  OpBuilder builder(m.getContext());
  m.walk([&](tf_device::ClusterFuncOp cluster_func) {
    auto replicate = cluster_func->getParentOfType<tf_device::ReplicateOp>();
    if (!replicate) return;
    auto mirrored_variable_indices_attr =