code: def side_chain_embs(
      self,
      pos_n,
      pos_ca,
      pos_c,
      pos_cb,
      pos_g,
      pos_d,
      pos_e,
      pos_z,
      pos_h,
  ):
      v1, v2, v3, v4, v5, v6, v7 = (
          pos_ca - pos_n,
          pos_cb - pos_ca,
          pos_g - pos_cb,
          pos_d - pos_g,
          pos_e - pos_d,
          pos_z - pos_e,
          pos_h - pos_z,
      )
  
      # five side chain torsion angles
      # We only consider the first four torsion angles in side chains since only the amino acid arginine has five side chain torsion angles, and the fifth angle is close to 0.
      angle1 = torch.unsqueeze(self.compute_dihedrals(v1, v2, v3), 1)
      angle2 = torch.unsqueeze(self.compute_dihedrals(v2, v3, v4), 1)
      angle3 = torch.unsqueeze(self.compute_dihedrals(v3, v4, v5), 1)
      angle4 = torch.unsqueeze(self.compute_dihedrals(v4, v5, v6), 1)
      angle5 = torch.unsqueeze(self.compute_dihedrals(v5, v6, v7), 1)
  
      side_chain_angles = torch.cat((angle1, angle2, angle3, angle4), 1)
      side_chain_embs = torch.cat(
          (torch.sin(side_chain_angles), torch.cos(side_chain_angles)),
          1,
      )
  
      return side_chain_embs
  
imports: import os.path
  import h5py
  import numpy
  import warnings
  from tqdm import tqdm
  import torch
  import torch.nn.functional
  from torch_geometric.data import Data
  from torch_geometric.data import InMemoryDataset
label_desc: Use image node_id 7 for calling the FOLDdataset obj's underlying member method code with example usage: obj.side_chain_embs(pos_n, pos_ca, pos_c, pos_cb, pos_g, pos_d, pos_e, pos_z, pos_h) and returns: side_chain_embs