class_docstr: Processor for entailment data sets used to train NLI models for zero shot intent classification.
class_docstr_tok: ["Processor","for","entailment","data","sets","used","to","train","NLI","models","for","zero","shot","intent","classification","."]
docstr: Gets a collection of `InputExample`s for the train set.
docstr_tok: ["Gets","a","collection","of","`","InputExample","`","s","for","the","train","set","."]
code: def get_train_examples(self, file_path: str):
      """Gets a collection of `InputExample`s for the train set."""
      return self._create_examples(self._read_tsv(file_path), "train")
  
imports: import os
  import pickle
  from typing import Dict, List, Optional
  import torch
  from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec
  from nemo.collections.nlp.data.data_utils.data_preprocessing import DataProcessor, fill_class_weights, get_freq_weights, get_label_stats
  from nemo.collections.nlp.data.glue_benchmark.data_processors import InputExample
  from nemo.collections.nlp.data.glue_benchmark.glue_benchmark_dataset import GLUEDataset
  from nemo.collections.nlp.parts.utils_funcs import tensor2list
  from nemo.core.neural_types import CategoricalValuesType, ChannelType, MaskType, NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 2 for calling the ZeroShotIntentProcessor obj's underlying member method code with example usage: obj.get_train_examples(file_path) and returns: self