How can i use the code to dock two proteins i am interested in? about diffdock-pp HOT 2 CLOSED

SCORE_MODEL_PATH: checkpoints/large_model_dips/fold_0/
CONFIDENCE_MODEL_PATH: checkpoints/large_model_dips/fold_0/
SAVE_PATH: ckpts/run_on_pdb_pairs
14:51:04 Starting Inference
14:51:04 Using Bound structures
data loading: 100%|█| 1/1 [00:00<00:00, 17549.39it
14:51:04 Loaded cached ESM embeddings
14:51:04 finished tokenizing residues with ESM
14:51:04 finished tokenizing all inputs
14:51:04 1 entries loaded
14:51:04 finished loading raw data
14:51:04 running inference
14:51:04 finished creating data splits
/usr/local/envs/diffdock_pp/lib/python3.10/site-packages/torch/jit/_check.py:181: UserWarning: The TorchScript type system doesn't support instance-level annotations on empty non-base types in `__init__`. Instead, either 1) use a type annotation in the class body, or 2) wrap the type in `torch.jit.Attribute`.
  warnings.warn("The TorchScript type system doesn't support "
14:51:06 loaded model with kwargs: 
checkpoint checkpoints/large_model_dips/fold_0/model_best_338669_140_31.084_30.347.pth
14:51:06 loaded checkpoint from checkpoints/large_model_dips/fold_0/model_best_338669_140_31.084_30.347.pth
14:51:10 loaded model with kwargs: 
checkpoint checkpoints/confidence_model_dips/fold_0/model_best_0_6_0.241_0.887.pth
14:51:10 loaded checkpoint from checkpoints/confidence_model_dips/fold_0/model_best_0_6_0.241_0.887.pth
14:51:10 finished loading model
args.temp_sampling: 2.439
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loader len:  40

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14:56:38 Finished Complex!
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14:56:38 Finished run run_on_pdb_pairs
temp sampling, temp_psi, temp_sigma_data_tr, temp_sigma_data_rot: (2.439, 0.216, 0.593, 0.228)
filtering_model_path: checkpoints/confidence_model_dips/fold_0/
Total time spent: 333.6226415634155
ligand_rmsd_summarized: {'mean': 70.51095, 'median': 70.51095, 'std': 0.0, 'lt1': 0.0, 'lt2': 0.0, 'lt5': 0.0, 'lt10': 0.0}
complex_rmsd_summarized: {'mean': 24.50482, 'median': 24.50482, 'std': 0.0, 'lt1': 0.0, 'lt2': 0.0, 'lt5': 0.0, 'lt10': 0.0}
interface_rmsd_summarized: {'mean': 23.4743, 'median': 23.4743, 'std': 0.0, 'lt1': 0.0, 'lt2': 0.0, 'lt5': 0.0, 'lt10': 0.0}
Traceback (most recent call last):
  File "/content/DiffDock-PP/src/main_inf.py", line 620, in <module>
    main()
  File "/content/DiffDock-PP/src/main_inf.py", line 354, in main
    dump_predictions(args,results)
  File "/content/DiffDock-PP/src/main_inf.py", line 383, in dump_predictions
    with open(args.prediction_storage, 'wb') as f:
FileNotFoundError: [Errno 2] No such file or directory: 'storage/run_on_pdb_pairs.pkl'

NUM_FOLDS=1  # number of seeds to try, default 5
SEED=0  # initial seed
CUDA=0  # will use GPUs from CUDA to CUDA + NUM_GPU - 1
NUM_GPU=1
BATCH_SIZE=1  # split across all GPUs
NUM_SAMPLES=40

NAME="single_pair_inference"  # change to name of config file
RUN_NAME="run_on_pdb_pairs"
CONFIG="config/${NAME}.yaml"

SAVE_PATH="ckpts/${RUN_NAME}"
VISUALIZATION_PATH="visualization/${RUN_NAME}"
STORAGE_PATH="storage/${RUN_NAME}.pkl"

FILTERING_PATH="checkpoints/confidence_model_dips/fold_0/"
SCORE_PATH="checkpoints/large_model_dips/fold_0/"

echo SCORE_MODEL_PATH: $SCORE_PATH
echo CONFIDENCE_MODEL_PATH: $SCORE_PATH
echo SAVE_PATH: $SAVE_PATH

python src/main_inf.py \
    --mode "test" \
    --config_file $CONFIG \
    --run_name $RUN_NAME \
    --save_path $SAVE_PATH \
    --batch_size $BATCH_SIZE \
    --num_folds $NUM_FOLDS \
    --num_gpu $NUM_GPU \
    --gpu $CUDA --seed $SEED \
    --logger "wandb" \
    --project "DiffDock Tuning" \
    --visualize_n_val_graphs 25 \
    --visualization_path $VISUALIZATION_PATH \
    --filtering_model_path $FILTERING_PATH \
    --score_model_path $SCORE_PATH \
    --num_samples $NUM_SAMPLES \
    --prediction_storage $STORAGE_PATH \
    #--entity coarse-graining-mit \
    #--debug True # load small dataset

    ---
 # file is parsed by inner-most keys only
 data:
     dataset: db5
     data_file: datasets/single_pair_dataset/splits_test.csv
     data_path: datasets/single_pair_dataset
     resolution: residue
     no_graph_cache: True
     knn_size: 20
     use_orientation_features: False
     multiplicity: 1
     use_unbound: False
 model:
     model_type: e3nn
     no_torsion: True
     no_batch_norm: True
     lm_embed_dim: 1280
     dropout: 0.0
     dynamic_max_cross: True
     cross_cutoff_weight: 3
     cross_cutoff_bias: 40
     cross_max_dist: 80
     num_conv_layers: 4
     ns: 16
     nv: 4
     dist_embed_dim: 32
     cross_dist_embed_dim: 32
     sigma_embed_dim: 32
     max_radius: 5.
 train:
     patience: 2000
     epochs: 2000
     lr: 1.e-3
     weight_decay: 0.
     tr_weight: 0.5
     rot_weight: 0.5
     tor_weight: 0.
     val_inference_freq: 10
     num_steps: 40
     actual_steps: 40
 diffusion:
     tr_s_min: 0.01
     tr_s_max: 30.0
     rot_s_min: 0.01
     rot_s_max: 1.65
     sample_train: True
     num_inference_complexes_train_data: 1200
 inference:
     mirror_ligand: False
     run_inference_without_confidence_model: False
     wandb_sweep: False
     no_final_noise: True
     # optimized for without conf_model
     temp_sampling: 2.439 # default 1.0. Set this to 1.0 to deactivate low temp sampling
     temp_psi: 0.216 # default 0.0
     temp_sigma_data_tr: 0.593 # default 0.5
     temp_sigma_data_rot:  0.228 # default 0.5
    #  temp_sampling: 5.33 # default 1.0. Set this to 1.0 to deactivate low temp sampling
    #  temp_psi: 1.05 # default 0.0
    #  temp_sigma_data_tr: 0.40 # default 0.5
    #  temp_sigma_data_rot:  0.64 # default 0.5

path,split
7c8d,test