docstr: "Implement abstract method.
docstr_tok: ["``","Implement","abstract","method","."]
code: def process_samples_from_single_path(self, filename):
      """ "Implement abstract method."""
      print_rank_0(" > Processing {} ...".format(filename))
  
      samples = []
      total = 0
      first = True
      is_test = False
      with open(filename, "r") as f:
          for line in f:
              row = line.strip().split("\t")
              if first:
                  first = False
                  if len(row) == 10:
                      is_test = True
                      print_rank_0(
                          "   reading {}, {} and {} columns and setting "
                          "labels to {}".format(
                              row[0].strip(),
                              row[8].strip(),
                              row[9].strip(),
                              self.test_label,
                          )
                      )
                  else:
                      print_rank_0(
                          "    reading {} , {}, {}, and {} columns "
                          "...".format(
                              row[0].strip(),
                              row[8].strip(),
                              row[9].strip(),
                              row[-1].strip(),
                          )
                      )
                  continue
  
              text_a = clean_text(row[8].strip())
              text_b = clean_text(row[9].strip())
              unique_id = int(row[0].strip())
              label = row[-1].strip()
              if is_test:
                  label = self.test_label
  
              assert len(text_a) > 0
              assert len(text_b) > 0
              assert label in LABELS
              assert unique_id >= 0
  
              sample = {
                  "text_a": text_a,
                  "text_b": text_b,
                  "label": LABELS[label],
                  "uid": unique_id,
              }
              total += 1
              samples.append(sample)
  
              if total % 50000 == 0:
                  print_rank_0(
                      "  > processed {} so far ...".format(total)
                  )
  
      print_rank_0(" >> processed {} samples.".format(len(samples)))
      return samples
  
imports: from megatron import print_rank_0
  from tasks.data_utils import clean_text
  from .data import GLUEAbstractDataset
label_desc: Use image node_id 2 for calling the MNLIDataset obj's underlying member method code with example usage: obj.process_samples_from_single_path(filename) and returns: samples