class_docstr: TextClassificationModel to be trained on two- or three-class textual entailment data, to be used for zero shot intent recognition.
class_docstr_tok: ["TextClassificationModel","to","be","trained","on","two-","or","three-class","textual","entailment","data",",","to","be","used","for","zero","shot","intent","recognition","."]
docstr: This method returns a list of pre-trained models which can be instantiated directly from NVIDIA's NGC cloud.
  
  Returns:
      List of available pre-trained models.
docstr_tok: ["This","method","returns","a","list","of","pre-trained","models","which","can","be","instantiated","directly","from","NVIDIA","'s","NGC","cloud",".","Returns",":","List","of","available","pre-trained","models","."]
code: def list_available_models(cls) -> Optional[PretrainedModelInfo]:
      """
      This method returns a list of pre-trained models which can be instantiated directly from NVIDIA's NGC cloud.
  
      Returns:
          List of available pre-trained models.
      """
      result = []
      result.append(
          PretrainedModelInfo(
              pretrained_model_name="zeroshotintent_en_bert_base_uncased",
              location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/zeroshotintent_en_bert_base_uncased/versions/1.4.1/files/zeroshotintent_en_bert_base_uncased.nemo",
              description="DialogueZeroShotIntentModel trained by fine tuning BERT-base-uncased on the MNLI (Multi-Genre Natural Language Inference) dataset, which achieves an accuracy of 84.9% and 84.8% on the matched and mismatched dev sets, respectively.",
          )
      )
      result.append(
          PretrainedModelInfo(
              pretrained_model_name="zeroshotintent_en_megatron_uncased",
              location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/zeroshotintent_en_megatron_uncased/versions/1.4.1/files/zeroshotintent_en_megatron_uncased.nemo",
              description="DialogueZeroShotIntentModel trained by fine tuning Megatron-BERT-345m=M-uncased on the MNLI (Multi-Genre Natural Language Inference) dataset, which achieves an accuracy of 90.0% and 89.9% on the matched and mismatched dev sets, respectively",
          )
      )
      return result
  
imports: import os
  from collections import defaultdict
  from typing import Dict, List, Optional, Union
  import numpy
  import torch
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  from nemo.collections.nlp.data.dialogue import DialogueSGDDataProcessor
  from nemo.collections.nlp.data.dialogue.data_processor.assistant_data_processor import DialogueAssistantDataProcessor
  from nemo.collections.nlp.data.dialogue.data_processor.design_data_processor import DialogueDesignDataProcessor
  from nemo.collections.nlp.data.dialogue.dataset.dialogue_zero_shot_intent_dataset import DialogueZeroShotIntentDataset
  from nemo.collections.nlp.data.zero_shot_intent_recognition.zero_shot_intent_dataset import ZeroShotIntentInferenceDataset, calc_class_weights_from_dataloader
  from nemo.collections.nlp.metrics.classification_report import ClassificationReport
  from nemo.collections.nlp.metrics.dialogue_metrics import DialogueGenerationMetrics
  from nemo.collections.nlp.models import TextClassificationModel
  from nemo.core.classes.common import PretrainedModelInfo
  from nemo.utils import logging
label_desc: Use image node_id 11 for calling the DialogueZeroShotIntentModel obj's underlying member method code with example usage: obj.list_available_models(cls) and returns: result