docstr: "Implement abstract method.
docstr_tok: ["``","Implement","abstract","method","."]
code: def process_samples_from_single_path(filename):
      """ "Implement abstract method."""
      print_rank_0(" > Processing {} ...".format(filename))
      samples = []
      total = 0
  
      with open(filename, "r", encoding="utf-8") as f:
          data = json.load(f)
          for row in data:
              question = normalize_question(row["question"])
              pos_context = row["positive_ctxs"][0]
  
              # Hard Negative Contexts
              if len(row["hard_negative_ctxs"]) > 0:
                  hard_neg_context = row["hard_negative_ctxs"]
              else:
                  hard_neg_context = []
  
              # Negative Contexts
              if len(row["negative_ctxs"]) > 0:
                  neg_context = row["negative_ctxs"]
              else:
                  neg_context = []
  
              answers = row["answers"]
              sample = {
                  "question": question,
                  "pos_context": pos_context,
                  "hard_negative_context": hard_neg_context,
                  "negative_context": neg_context,
                  "answers": answers,
              }
              total += 1
              samples.append(sample)
  
              if total % 5000 == 0:
                  print_rank_0(
                      "  > processed {} so far ...".format(total)
                  )
  
      print_rank_0(" >> processed {} samples.".format(len(samples)))
      return samples
  
imports: import json
  import random
  from abc import ABC
  from abc import abstractmethod
  import numpy
  from torch.utils.data import Dataset
  from megatron import print_rank_0, get_args
  from megatron.data.biencoder_dataset_utils import make_attention_mask
label_desc: Use image node_id 2 for calling the NQSupervisedDataset obj's underlying member method code with example usage: obj.process_samples_from_single_path(filename) and returns: samples