text: import os
  import torch
  
  # checks
  print('working directory is:')
  print(os.getcwd())
  print('is CUDA available?')
  print(torch.cuda.is_available())
  
  # imports
  from chemprop.args import TrainArgs
  from chemprop.train.pdts import pdts
  
  # instantiate args class and load from dict
  args = TrainArgs()
  args.from_dict({
      'dataset_type': 'regression',
      'data_path': '/home/willlamb/chempropBayes/data/qm9.csv'
  })
  
  
  
  ##################### ARGS #####################
  
  # architecture
  args.hidden_size = 500
  args.depth = 5
  args.ffn_num_layers = 3
  args.activation = 'ReLU'
  args.ffn_hidden_size = args.hidden_size
  args.features_path = None
  args.features_generator = None
  args.atom_messages = False
  args.undirected = False
  args.bias = False
  
  # data
  args.max_data_size = 100000
  args.data_seeds = [0,1,2,3,4]
  args.split_type = 'random'
  args.split_sizes = (0.05, 0.95)
  
  # metric
  args.metric = 'mae'
  
  # run seeds
  args.pytorch_seeds = [0,1,2,3,4]
  
  ################################################
  
  # names and directories
  args.results_dir = '/home/willlamb/results_pdts/bbp_thom'
  args.save_dir = '/home/willlamb/checkpoints_pdts/bbp_thom'
  args.checkpoint_path = '/home/willlamb/checkpoints_pdts/map_greedy'
  args.wandb_proj = 'lanterne_bbp'
  args.wandb_name = 'bbp_thom'
  args.thompson = True
  
  
  ### bbp ###
  args.bbp = True
  args.samples = 50
  
  args.pdts = True
  args.pdts_batches = 30
  
  args.epochs_init_map = 0
  args.epochs_init = 1000
  args.epochs = 100
  
  args.lr = 1e-4
  
  args.prior_sig_bbp = 0.15
  args.rho_min_bbp = -5.5
  args.rho_max_bbp = -5
  args.samples_bbp = 5
  
  
  ################################################
  
  
  # run
  results = pdts(args, model_idx = 0)
  