code: def available_models():
      models = {m.name: m for m in [efficientnet_b0]}
      return models
  
imports: import os
  import argparse
  import random
  from copy import deepcopy
  import torch.backends.cudnn
  import torch.distributed
  import torch.nn.parallel
  import torch.optim
  import torch.utils.data
  import torch.utils.data.distributed
  import image_classification.logger
  from image_classification.smoothing import LabelSmoothing
  from image_classification.mixup import NLLMultiLabelSmooth, MixUpWrapper
  from image_classification.dataloaders import *
  from image_classification.training import *
  from image_classification.utils import *
  from image_classification.models import efficientnet_b0
  from image_classification.optimizers import get_optimizer, lr_cosine_policy, lr_linear_policy, lr_step_policy
  from image_classification.gpu_affinity import set_affinity, AffinityMode
  import dllogger
label_desc: Use image node_id 1 for calling a global function with example usage: available_models() and returns: models