code: def __init__(
      self,
      num_units=30,
      nonlin=nn.ReLU(),
      dropout=0.5,
  ):
      super().__init__()
      self.num_units = num_units
      self.nonlin = nonlin
      self.dropout = dropout
  
      self.dense0 = nn.Linear(20, num_units)
      self.nonlin = nonlin
      self.dropout = nn.Dropout(dropout)
      self.dense1 = nn.Linear(num_units, num_units)
      self.output = nn.Linear(num_units, 2)
      self.softmax = nn.Softmax(dim=-1)
  
imports: import os
  import pickle
  from contextlib import contextmanager
  import numpy
  import torch
  from huggingface_hub import HfApi, create_repo, hf_hub_download
  from sklearn.datasets import fetch_20newsgroups, make_classification
  from sklearn.metrics import accuracy_score
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import Pipeline
  from torch import nn
  from transformers import AutoModelForSequenceClassification
  from skorch import NeuralNetClassifier
  from skorch.hf import HuggingfacePretrainedTokenizer
  from skorch.callbacks import TrainEndCheckpoint
  from skorch.hf import HfHubStorage
label_desc: Use image node_id 1 to create a new ClassifierModule object from inherited base classes: nn with example: obj = ClassifierModule(num_units, nonlin, dropout)