code: def gen_mol_from_one_shot_tensor(
      adj,
      x,
      atomic_num_list,
      correct_validity=True,
      largest_connected_comp=True,
  ):
      r"""Construct molecules from the node tensors and adjacency tensors generated by one-shot molecular graph generation methods.
  
      Args:
          adj (Tensor): The adjacency tensor with shape [:obj:`number of samples`, :obj:`number of possible bond types`, :obj:`maximum number of atoms`, :obj:`maximum number of atoms`].
          x (Tensor): The node tensor with shape [:obj:`number of samples`, :obj:`number of possible atom types`, :obj:`maximum number of atoms`].
          atomic_num_list (list): A list to specify what atom each channel of the 2nd dimension of :obj: `x` corresponds to.
          correct_validity (bool, optional): Whether to use the validity correction introduced by the paper `MoFlow: an invertible flow model for generating molecular graphs <https://arxiv.org/pdf/2006.10137.pdf>`_. (default: :obj:`True`)
          largest_connected_comp (bool, optional): Whether to use the largest connected component as the final molecule in the validity correction.(default: :obj:`True`)
  
      :rtype: A list of rdkit mol object. The length of the list is :obj:`number of samples`.
  
      Examples
      --------
      >>> adj = torch.rand(2, 4, 38, 38)
      >>> x = torch.rand(2, 10, 38)
      >>> atomic_num_list = [6, 7, 8, 9, 15, 16, 17, 35, 53, 0]
      >>> gen_mols = gen_mol_from_one_shot_tensor(adj, x, atomic_num_list)
      >>> gen_mols
      [<rdkit.Chem.rdchem.Mol>, <rdkit.Chem.rdchem.Mol>]
      """
      x = x.permute(0, 2, 1)
      adj = adj.cpu().detach().numpy()
      x = x.cpu().detach().numpy()
      if not correct_validity:
          gen_mols = [
              construct_mol(x_elem, adj_elem, atomic_num_list)
              for x_elem, adj_elem in zip(x, adj)
          ]
      else:
          gen_mols = []
          for x_elem, adj_elem in zip(x, adj):
              mol = construct_mol(x_elem, adj_elem, atomic_num_list)
              cmol = correct_mol(mol)
              vcmol = valid_mol_can_with_seg(
                  cmol, largest_connected_comp=largest_connected_comp
              )
              gen_mols.append(vcmol)
  
      return gen_mols
  
imports: import re
  import numpy
  from rdkit import Chem
label_desc: Use image node_id 5 for calling a global function with example usage: gen_mol_from_one_shot_tensor(adj, x, atomic_num_list, correct_validity, largest_connected_comp) and returns: gen_mols