code: def forward(self, images: Tensor) -> Tensor:
      if self.freeze_model:
          with torch.no_grad():
              features = self.model.forward(images).flatten(start_dim=1)
      else:
          features = self.model.forward(images).flatten(start_dim=1)
      output: Tensor = self.classification_head(features)
      return output
  
file_path: lightly/lightly/utils/benchmarking/linear_classifier.py
imports: from typing import Any, Dict, List, Tuple, Union
  import torch
  from pytorch_lightning import LightningModule
  from torch import Tensor
  from torch.nn import CrossEntropyLoss, Linear, Module
  from torch.optim import SGD, Optimizer
  from lightly.utils.benchmarking.topk import mean_topk_accuracy
  from lightly.utils.scheduler import CosineWarmupScheduler
label_desc: Use image node_id 2 for calling the LinearClassifier obj's underlying member method code with example usage: obj.forward(images) and returns: output