class_docstr: Class for Hugging Face sequence classification models
  
  Attributes:
      model_path: path to HF model
      tokenizer_path: path to HF tokenizer
      name: string identifier for the model. Lowercase letters and numbers.
          No spaces/special characters except dashes.
      description: String description of the model.
      tasks: List of supported task strings
      details: Dictionary of additional details about the model
      model_class: Class used to initialise model
      tokenizer_class: Class used to initialise tokenizer
      device: Device for model. Defaults to "cuda" if available.
class_docstr_tok: ["Class","for","Hugging","Face","sequence","classification","models","Attributes",":","model_path",":","path","to","HF","model","tokenizer_path",":","path","to","HF","tokenizer","name",":","string","identifier","for","the","model",".","Lowercase","letters","and","numbers",".","No","spaces\/special","characters","except","dashes",".","description",":","String","description","of","the","model",".","tasks",":","List","of","supported","task","strings","details",":","Dictionary","of","additional","details","about","the","model","model_class",":","Class","used","to","initialise","model","tokenizer_class",":","Class","used","to","initialise","tokenizer","device",":","Device","for","model",".","Defaults","to","``","cuda","''","if","available","."]
code: def list_models():
      from .models_list import models
  
      return models
  
file_path: backprop/backprop/models/hf_seq_tc_model/model.py
imports: import torch
  import pytorch_lightning
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  from transformers.optimization import AdamW
  from backprop.models import HFModel
  from typing import List, Union, Dict
label_desc: Use image node_id 2 for calling the HFSeqTCModel obj's underlying member method code with example usage: obj.list_models() and returns: models