code: def forward(self, samples, targets):
      samples_device = samples.device
      samples = samples.to(dtype=self.dtype, device=self.device)
      if self.device == _CPU_DEVICE:
          samples = samples.numpy()
      probas = self.estimator.predict_proba(samples)
      return {
          "preds": torch.from_numpy(probas).to(samples_device),
          "target": targets,
      }
  
imports: import argparse
  import gc
  import logging
  import sys
  import time
  from typing import List, Optional
  from cuml.linear_model import LogisticRegression
  import torch
  import torch.backends.cudnn
  import torch.distributed
  from torch import nn
  from torch.utils.data import TensorDataset
  from torchmetrics import MetricTracker
  from dinov2.data import make_dataset
  from dinov2.data.transforms import make_classification_eval_transform
  from dinov2.distributed import get_global_rank, get_global_size
  from dinov2.eval.metrics import MetricType, build_metric
  from dinov2.eval.setup import get_args_parser
  from dinov2.eval.setup import setup_and_build_model
  from dinov2.eval.utils import evaluate, extract_features
  from dinov2.utils.dtype import as_torch_dtype
label_desc: Use image node_id 2 for calling the LogRegModule obj's underlying member method code with example usage: obj.forward(samples, targets) and returns: dict