code: def write_predictions_to_typedb(predicted_links, tx):
      """
      Take predictions from the ML model, and insert representations of those predictions back into the graph.
  
      Args:
          predicted_links: pairs of concepts that are predicted links
          tx: TypeDB write transaction to use
  
      Returns: None
  
      """
      for predicted_link in predicted_links:
          person = predicted_link["person"]
          disease = predicted_link["disease"]
          query = (
              f"match "
              f"$p iid {person.iid}; "
              f"$d iid {disease.iid}; "
              f"insert "
              f"$pd(patient: $p, diagnosed-disease: $d) isa predicted-diagnosis;"
          )
          tx.query().insert(query)
      tx.commit()
  
imports: import argparse
  import inspect
  import os
  import networkx
  import torch
  import torch.nn.functional
  import torch_geometric.transforms
  from torch import as_tensor
  from torch.utils.tensorboard import SummaryWriter
  from torch_geometric.nn import HGTConv
  from typedb.client import *
  from examples.diagnosis.dataset.generate import generate_example_data
  from typedb_ml.networkx.query_graph import QueryGraph, Query
  from typedb_ml.pytorch_geometric.dataset.dataset import DataSet
  from typedb_ml.pytorch_geometric.transform.binary_link_prediction import LinkPredictionLabeller, binary_relations_to_edges, binary_link_prediction_edge_triplets
  from typedb_ml.pytorch_geometric.transform.common import clear_unneeded_fields, store_concepts_by_type
  from typedb_ml.pytorch_geometric.transform.encode import FeatureEncoder, CategoricalEncoder, ContinuousEncoder
  from typedb_ml.typedb.load import load_typeql_file, FileType
  from typedb_ml.typedb.type import get_thing_types
label_desc: Use image node_id 4 for calling a global function with example usage: write_predictions_to_typedb(predicted_links, tx) without return types