code: def fast_adapt(
      batch, learner, loss, adaptation_steps, shots, ways, device
  ):
      data, labels = batch
      data, labels = data.to(device), labels.to(device)
  
      # Separate data into adaptation/evalutation sets
      adaptation_indices = torch.zeros(data.size(0), dtype=bool)
      adaptation_indices[torch.arange(shots * ways) * 2] = True
      evaluation_indices = ~adaptation_indices
      adaptation_data, adaptation_labels = (
          data[adaptation_indices],
          labels[adaptation_indices],
      )
      evaluation_data, evaluation_labels = (
          data[evaluation_indices],
          labels[evaluation_indices],
      )
  
      # Adapt the model
      for step in range(adaptation_steps):
          train_error = loss(
              learner(adaptation_data), adaptation_labels
          )
          learner.adapt(train_error)
  
      # Evaluate the adapted model
      predictions = learner(evaluation_data)
      valid_error = loss(predictions, evaluation_labels)
      valid_accuracy = accuracy(predictions, evaluation_labels)
      return valid_error, valid_accuracy
  
file_path: pytorch-lightning/examples/fabric/meta_learning/train_torch.py
imports: import os
  import random
  import cherry
  import learn2learn
  import torch
  import torch.distributed
label_desc: Use image node_id 2 for calling a global function with example usage: fast_adapt(batch, learner, loss, adaptation_steps, shots, ways, device) and returns: valid_error,  valid_accuracy