class_docstr: Vision transformer module
  
  Parameters
  ----------
  model_name : str (default='google/vit-base-patch32-224-in21k')
    Name of the feature extractor on Hugging Face Hub.
  
  num_classes : int (default=3)
    Number of target classes to classify.
class_docstr_tok: ["Vision","transformer","module","Parameters","--","--","--","--","--","model_name",":","str","(","default='google\/vit-base-patch32-224-in21k","'",")","Name","of","the","feature","extractor","on","Hugging","Face","Hub",".","num_classes",":","int","(","default=3",")","Number","of","target","classes","to","classify","."]
code: def __init__(
      self,
      model_name="google/vit-base-patch32-224-in21k",
      num_classes=3,
  ):
      super().__init__()
      self.model = ViTForImageClassification.from_pretrained(
          model_name, num_labels=num_classes
      )
  
imports: from functools import partial
  import pickle
  import fire
  import numpy
  import torch
  from datasets import load_dataset
  from skorch.helper import parse_args
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.metrics import accuracy_score
  from sklearn.pipeline import Pipeline
  from skorch import NeuralNetClassifier
  from skorch.callbacks import ProgressBar, LRScheduler
  from torch import nn
  from torch.optim.lr_scheduler import LambdaLR
  from transformers import ViTFeatureExtractor, ViTForImageClassification
label_desc: Use image node_id 1 to create a new VitModule object from inherited base classes: nn with example: obj = VitModule(model_name, num_classes)