code: def lr_lambda(
      current_step: int, num_warmup_steps, num_training_steps
  ):
      if current_step < num_warmup_steps:
          return float(current_step) / float(max(1, num_warmup_steps))
      return max(
          0.0,
          float(num_training_steps - current_step)
          / float(max(1, num_training_steps - num_warmup_steps)),
      )
  
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 2 for calling a global function with example usage: lr_lambda(current_step, num_warmup_steps, num_training_steps) and returns: max, unknown