code: def get_benchmark_config(checkpoint_activation=True):
      return {
          "epochs": 1,
          "lr": 0.001,  # learning rate
          "batch_size": 8,
          "criterion": nn.CrossEntropyLoss(),
          "checkpoint_activation": checkpoint_activation,
          "num_microbatches": 1,
          "slices": 3,
      }
  
imports: import torch.nn
  from fairscale.optim import GradScaler
label_desc: Use image node_id 2 for calling the Offload_Transformer obj's underlying member method code with example usage: obj.get_benchmark_config(checkpoint_activation) and returns: dict