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 get_embed(self, model, loader):
      model.eval()
      model.to(self.comp_embed_on)
      ret, y = [], []
      with torch.no_grad():
          for data in loader:
              y.append(data.y.numpy())
              data.to(self.comp_embed_on)
              embed = model(data)
              ret.append(embed.cpu().numpy())
  
      model.to(self.device)
      ret = np.concatenate(ret, 0)
      y = np.concatenate(y, 0)
      return ret, y
  
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 8 for calling the NodeUnsupervised obj's underlying member method code with example usage: obj.get_embed(model, loader) and returns: ret,  y