class_docstr: Designed for classification tasks such as intent/domain classification as well as slot tagging
  
  Dataset Class 
      1. Performs Model-dependent (but Data-independent) operations (tokenization etc)
      2. This can allow the same model preprocessing for multiple datasources
      3. Users can configurate which labels to use for modelling 
          (e.g. intent classification, slot filling or both together etc)
class_docstr_tok: ["Designed","for","classification","tasks","such","as","intent\/domain","classification","as","well","as","slot","tagging","Dataset","Class","1",".","Performs","Model-dependent","(","but","Data-independent",")","operations","(","tokenization","etc",")","2",".","This","can","allow","the","same","model","preprocessing","for","multiple","datasources","3",".","Users","can","configurate","which","labels","to","use","for","modelling","(","e.g",".","intent","classification",",","slot","filling","or","both","together","etc",")"]
code: def get_n_tokens_in_sentence(self, sentence):
      encodings_dict = self.tokenizer.tokenizer(
          sentence,
          truncation=True,
          max_length=self.cfg.max_seq_length,
          padding=False,
          return_tensors="pt",
      )
      output = torch.squeeze(encodings_dict["input_ids"])
      return len(output) if len(output.size()) > 0 else 0
  
file_path: NeMo/nemo/collections/nlp/data/dialogue/dataset/dialogue_gpt_classification_dataset.py
imports: import copy
  import random
  from collections import defaultdict
  import torch
  from nemo.collections.nlp.data.dialogue.dataset.dialogue_dataset import DialogueDataset
  from nemo.utils import logging
label_desc: Use image node_id 4 for calling the DialogueGPTClassificationDataset obj's underlying member method code with example usage: obj.get_n_tokens_in_sentence(sentence) and returns: unknown