code: def __init__(
      self,
      model,
      device,
      num_updates_per_iter,
      num_layers,
      task_learning_rate,
      learnable_learning_rates,
      meta_learning_rate,
      total_epochs,
      min_learning_rate,
      learning_rate_scheduler,
      second_order,
      use_multi_step_loss_optimization,
      multi_step_loss_num_epochs,
  ):
      self.model = model
      self.num_updates_per_iter = num_updates_per_iter
      self.learning_rates = nn.Parameter(
          data=torch.ones(
              (num_updates_per_iter, num_layers), device=device
          )
          * task_learning_rate,
          requires_grad=learnable_learning_rates,
      )
  
      self.optimizer = optim.Adam(
          self.model.get_weights() + [self.learning_rates],
          lr=meta_learning_rate,
          amsgrad=False,
      )
  
      self.learning_rate_scheduler = learning_rate_scheduler
      self.scheduler = optim.lr_scheduler.CosineAnnealingLR(
          optimizer=self.optimizer,
          T_max=total_epochs,
          eta_min=min_learning_rate,
      )
  
      self.second_order = second_order
      self.use_multi_step_loss_optimization = (
          use_multi_step_loss_optimization
      )
      self.multi_step_loss_num_epochs = multi_step_loss_num_epochs
      self.device = device
  
file_path: PARL/benchmark/torch/maml++/maml_algorithm.py
imports: import torch
  from torch import nn
  from torch import optim
  import torch.nn.functional
  import numpy
  import parl
label_desc: Use image node_id 1 to create a new MAML object from inherited base classes: parl with example: obj = MAML(model, device, num_updates_per_iter, num_layers, task_learning_rate, learnable_learning_rates, meta_learning_rate, total_epochs, min_learning_rate, learning_rate_scheduler, second_order, use_multi_step_loss_optimization, multi_step_loss_num_epochs)