class_docstr: Image feature extractor
  
  Parameters
  ----------
  model_name : str (default='google/vit-base-patch32-224-in21k')
    Name of the feature extractor on Hugging Face Hub.
  
  device : str (default='cuda')
    Computation device, typically 'cuda' or 'cpu'.
class_docstr_tok: ["Image","feature","extractor","Parameters","--","--","--","--","--","model_name",":","str","(","default='google\/vit-base-patch32-224-in21k","'",")","Name","of","the","feature","extractor","on","Hugging","Face","Hub",".","device",":","str","(","default='cuda","'",")","Computation","device",",","typically","'cuda","'","or","'cpu","'","."]
code: def __init__(
      self,
      model_name="google/vit-base-patch32-224-in21k",
      device="cuda",
  ):
      self.model_name = model_name
      self.device = device
  
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 FeatureExtractor object from inherited base classes: BaseEstimator, TransformerMixin with example: obj = FeatureExtractor(model_name, device)