code: def forward_teacher(self, x: Tensor) -> Tuple[Tensor, Tensor]:
      features = self(x).flatten(start_dim=1)
      projections = self.projection_head(features)
      return features, projections
  
imports: import copy
  from typing import List, Tuple
  import torch
  from pytorch_lightning import LightningModule
  from torch import Tensor
  from torch.nn import Identity
  from torchvision.models import resnet50
  from lightly.loss import NegativeCosineSimilarity
  from lightly.models.modules import BYOLPredictionHead, BYOLProjectionHead
  from lightly.models.utils import get_weight_decay_parameters, update_momentum
  from lightly.transforms import BYOLTransform
  from lightly.utils.benchmarking import OnlineLinearClassifier
  from lightly.utils.lars import LARS
  from lightly.utils.scheduler import CosineWarmupScheduler, cosine_schedule
label_desc: Use image node_id 3 for calling the BYOL obj's underlying member method code with example usage: obj.forward_teacher(x) and returns: features,  projections