class_docstr: TSV data loader for files having a single input text and a label.
  
  Files must be in TSV format with 2 columns in this order:
  1. Input text.
  2. Numeric label.
  
  Exported examples have 2 output keys: "sentence" and "label".
code: def load_datapoints(self, path: str):
      with open(path) as fd:
          df = pd.read_csv(
              fd, sep="\t", header=None, names=["sentence", "label"]
          )
      # pylint: disable=g-complex-comprehension
      return [
          {
              "sentence": row["sentence"],
              "label": self.LABELS[row["label"]],
          }
          for _, row in df.iterrows()
      ]
  
file_path: lit/lit_nlp/examples/is_eval/datasets.py
imports: from lit_nlp.api import dataset
  from lit_nlp.api import types
  import pandas
label_desc: Use image node_id 2 for calling the SingleInputClassificationFromTSV obj's underlying member method code with example usage: obj.load_datapoints(path) and returns: unknown