class_docstr: The evaluation interface for unsupervised graph representation learning evaluated with 
  linear classification. You can refer to `the benchmark code 
  <https://github.com/divelab/DIG/tree/dig/benchmarks/sslgraph>`_ 
  for examples of usage.
  
  Args:
      full_dataset (torch_geometric.data.Dataset): The graph classification dataset.
      train_mask (Tensor, optional): Boolean tensor of shape :obj:`[n_nodes,]`, indicating 
          nodes for training. Set to :obj:`None` if included in dataset.
          (default: :obj:`None`)
      val_mask (Tensor, optional): Boolean tensor of shape :obj:`[n_nodes,]`, indicating 
          nodes for validation. Set to :obj:`None` if included in dataset.
          (default: :obj:`None`)
      test_mask (Tensor, optional): Boolean tensor of shape :obj:`[n_nodes,]`, indicating 
          nodes for test. Set to :obj:`None` if included in dataset. (default: :obj:`None`)
      classifier (string, optional): Linear classifier for evaluation, :obj:`"SVC"` or 
          :obj:`"LogReg"`. (default: :obj:`"LogReg"`)
      log_interval (int, optional): Perform evaluation per k epochs. (default: :obj:`1`)
      device (int, or torch.device, optional): Device for computation. (default: :obj:`None`)
      **kwargs (optional): Training and evaluation configs in :meth:`setup_train_config`.
      
  Examples
  --------
  >>> node_dataset = get_node_dataset("Cora") # using default train/test split
  >>> evaluator = NodeUnsupervised(node_dataset, log_interval=10, device=0)
  >>> evaluator.evaluate(model, encoder)
  
  >>> node_dataset = SomeDataset()
  >>> # Using your own dataset or with different train/test split
  >>> train_mask, val_mask, test_mask = torch.Tensor([...]), torch.Tensor([...]), torch.Tensor([...])
  >>> evaluator = NodeUnsupervised(node_dataset, train_mask, val_mask, test_mask, log_interval=10, device=0)
  >>> evaluator.evaluate(model, encoder)
code: def svc_clf(self, train_embs, train_lbls, test_embs, test_lbls):
      if self.search:
          params = {"C": [0.001, 0.01, 0.1, 1, 10, 100, 1000]}
          classifier = GridSearchCV(
              SVC(), params, cv=5, scoring="accuracy", verbose=0
          )
      else:
          classifier = SVC(C=10)
  
      classifier.fit(train_embs, train_lbls)
      acc = accuracy_score(test_lbls, classifier.predict(test_embs))
  
      return acc
  
imports: import copy
  import torch
  import numpy
  import torch.nn
  from torch_geometric.loader import DataLoader
  from sklearn import preprocessing
label_desc: Use image node_id 6 for calling the NodeUnsupervised obj's underlying member method code with example usage: obj.svc_clf(train_embs, train_lbls, test_embs, test_lbls) and returns: acc