code: def from_id(
      cls, root, task_id, transform=None, target_transform=None
  ):
      import pandas as pd
  
      root = os.path.join(os.path.expanduser(root), TCGA.folder)
      gene_filepath = os.path.join(root, TCGA.gene_expression_filename)
      if not os.path.isfile(gene_filepath):
          raise IOError()
  
      label, cancer = task_id
  
      processed_folder = os.path.join(
          root, "clinicalMatrices", "processed"
      )
      filename = "{0}.tsv".format(
          TCGA.clinical_matrix_filename.format(cancer)
      )
      filepath = os.path.join(processed_folder, filename)
      if not os.path.isfile(filepath):
          raise IOError()
  
      dataframe = pd.read_csv(filepath, sep="\t", index_col=0, header=0)
      labels = dataframe[label].dropna().astype("category")
  
      with h5py.File(gene_filepath, "r") as f:
          data = f["expression_data"][labels.index]
  
      return cls(
          task_id,
          data,
          labels.cat.codes.tolist(),
          labels.cat.categories.tolist(),
          transform=transform,
          target_transform=target_transform,
      )
  
imports: import os
  import json
  import h5py
  import numpy
  import torch
  import copy
  from ordered_set import OrderedSet
  from torchmeta.utils.data import Task, MetaDataset
  from torchmeta.datasets.utils import get_asset
label_desc: Use image node_id 1 for calling the TCGATask obj's underlying member method code with example usage: obj.from_id(cls, root, task_id, transform, target_transform) and returns: cls