docstr: Helper function that returns a list of lists, each of which contains the information for all candidates for each sample
docstr_tok: ["Helper","function","that","returns","a","list","of","lists",",","each","of","which","contains","the","information","for","all","candidates","for","each","sample"]
code: def _select_field(samples, field):
      """Helper function that returns a list of lists, each of which contains the information for all candidates for each sample"""
      return [
          [cand[field] for cand in sample["candidate_features"]]
          for sample in samples
      ]
  
imports: import torch
  import os
  import numpy
  from pytorch_transformers.modeling_bert import BertPreTrainedModel, BertConfig, BertModel
  from pytorch_transformers.tokenization_bert import BertTokenizer
  from torch.utils.data import DataLoader, SequentialSampler, TensorDataset
  from torch import nn
  from torch.nn import CrossEntropyLoss, MSELoss
  from tqdm import tqdm
  from pytorch_transformers.optimization import AdamW, WarmupLinearSchedule
  from pytorch_transformers.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
label_desc: Use image node_id 8 for calling the BertReranker obj's underlying member method code with example usage: obj._select_field(samples, field) and returns: unknown