class_docstr: BERT language model pretraining.
class_docstr_tok: ["BERT","language","model","pretraining","."]
docstr: This method returns a list of pre-trained model 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","model","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 model which can be instantiated directly from NVIDIA's NGC cloud.
  
      Returns:
          List of available pre-trained models.
      """
      result = []
  
      result.append(
          PretrainedModelInfo(
              pretrained_model_name="bertbaseuncased",
              location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/bertbaseuncased/versions/1.0.0rc1/files/bertbaseuncased.nemo",
              description="The model was trained EN Wikipedia and BookCorpus on a sequence length of 512.",
          )
      )
  
      result.append(
          PretrainedModelInfo(
              pretrained_model_name="bertlargeuncased",
              location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/bertlargeuncased/versions/1.0.0rc1/files/bertlargeuncased.nemo",
              description="The model was trained EN Wikipedia and BookCorpus on a sequence length of 512.",
          )
      )
      return result
  
imports: import os
  from typing import Dict, Optional
  import torch
  from omegaconf import DictConfig, OmegaConf
  from pytorch_lightning import Trainer
  from torch.utils.data import DataLoader
  from nemo.collections.common.losses import AggregatorLoss, CrossEntropyLoss, SmoothedCrossEntropyLoss
  from nemo.collections.common.metrics import Perplexity
  from nemo.collections.nlp.data.language_modeling.lm_bert_dataset import BertPretrainingDataset, BertPretrainingPreprocessedDataloader
  from nemo.collections.nlp.modules.common import BertPretrainingTokenClassifier, SequenceClassifier
  from nemo.collections.nlp.modules.common.lm_utils import get_lm_model
  from nemo.collections.nlp.modules.common.tokenizer_utils import get_tokenizer
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.core.classes.modelPT import ModelPT
  from nemo.core.neural_types import NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 16 for calling the BERTLMModel obj's underlying member method code with example usage: obj.list_available_models(cls) and returns: result