class_docstr: The breast cancer (BRCA TCGA Pan-Cancer Atlas) dataset consisting of
  patients with survival information and gene expression data from
  `cBioPortal <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4160307/>`_
  and a network of biological interactions between those nodes from
  `Pathway Commons <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7145667/>`_.
  The dataset contains the gene features of 1,082 patients, and the overall
  survival time (in months) of each patient as label.
  
  Pre-processing and example model codes on how to use this dataset can be
  found `here <https://github.com/cannin/pyg_pathway_commons_cbioportal>`_.
  
  Args:
      root (str): Root directory where the dataset should be saved.
      transform (callable, optional): A function/transform that takes in an
          :obj:`torch_geometric.data.Data` object and returns a transformed
          version. The data object will be transformed before every access.
          (default: :obj:`None`)
      pre_transform (callable, optional): A function/transform that takes in
          an :obj:`torch_geometric.data.Data` object and returns a
          transformed version. The data object will be transformed before
          being saved to disk. (default: :obj:`None`)
      pre_filter (callable, optional): A function that takes in an
          :obj:`torch_geometric.data.Data` object and returns a boolean
          value, indicating whether the data object should be included in the
          final dataset. (default: :obj:`None`)
      force_reload (bool, optional): Whether to re-process the dataset.
          (default: :obj:`False`)
  
  **STATS:**
  
  .. list-table::
      :widths: 10 10 10 10
      :header-rows: 1
  
      * - #graphs
        - #nodes
        - #edges
        - #features
      * - 1,082
        - 9,288
        - 271,771
        - 1,082
code: def process(self) -> None:
      import pandas as pd
  
      graph_feat = pd.read_csv(self.raw_paths[0], index_col=0).values
      graph_feat = torch.from_numpy(graph_feat).to(torch.float)
      graph_labels = np.loadtxt(self.raw_paths[1], delimiter=",")
      graph_label = torch.from_numpy(graph_labels).to(torch.float)
      edge_index = torch.load(self.raw_paths[2])
  
      data_list = []
      for x, y in zip(graph_feat, graph_label):
          data = Data(x=x.view(-1, 1), edge_index=edge_index, y=y)
  
          if self.pre_filter is not None and not self.pre_filter(data):
              continue
          if self.pre_transform is not None:
              data = self.pre_transform(data)
  
          data_list.append(data)
  
      self.save(data_list, self.processed_paths[0])
  
file_path: pytorch_geometric/torch_geometric/datasets/brca_tgca.py
imports: import os
  import os.path
  from typing import Callable, List, Optional
  import numpy
  import torch
  from torch_geometric.data import Data, InMemoryDataset, download_url, extract_zip
  from torch_geometric.io import fs
label_desc: Use image node_id 5 for calling the BrcaTcga obj's underlying member method code with example usage: obj.process() without return types