result.replaceAllUsesWith(if_op.getOperand(then_aliased_input + 1));
    }
  }
  if_op.replaceAllUsesWith(new_if);
  if_op.erase();
  return success();
}

// Handles stack usage by a tf.StatefulPartitionedCall or a tf.PartitionedCall.
// It will first check if the callee was previously handled, and try to reuse
// that result if so. Otherwise, it will clone and convert the callee function,

  // CHECK: [[results_0:%.*]] = call @callee(
  // CHECK-SAME: (!tf_mlrt.tensor) -> !tf_mlrt.tensor
  %1 = "tf.StatefulPartitionedCall"(%0) {config = "", config_proto = "", executor_type = "", f = @callee} : (tensor<i32>) -> (tensor<i32>)
  // CHECK-NEXT: [[results_1:%.*]] = call @callee(
  // CHECK-SAME: (!tf_mlrt.tensor) -> !tf_mlrt.tensor

                                                arg_buffer_size_is_fixed(i)};
    }
  }
  UpdateFuncType(func);
}

// Holds information about a decomposed callee function for
// PartitionedCall/StatefulPartitionedCall.
struct PartitionedCallDecompositionInfo {
  bool signature_change;
  func::FuncOp decomposed_callee;
  llvm::SmallDenseMap<int64_t, int64_t> buffer_arg_to_size_arg;

    // (checked later).
    if (auto device_type_attr =
            op.getAttrOfType<StringAttr>(mlir::TF::kCompileDeviceTypeAttr)) {
      // tf.StatefulPartitionedCall ops with and without
      // _tpu_replicate attributes may exist in the same graph. Ops without
      // the attribute but with _XlaMustCompile=true would have
      // _xla_compile_device_type="" after