code: def __getitem__(self, idx):
      # self.params is a dict containig lists (inputs, outputs) and fixed values (e.g. max_input_length)
      # Line here gets [idx] of lists, as well as fixed values, as a dict to be passed to model for processing.
      params = {
          k: (v if type(v) != list else v[idx])
          for k, v in self.params.items()
      }
      inp = self.process_batch(params, task="text-classification")
  
      return {**inp}
  
imports: import torch
  from torch.utils.data import Dataset
  from PIL import Image
  import numpy
label_desc: Use image node_id 3 for calling the SingleLabelTextClassificationDataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: dict