class_docstr: Apply SMOTE
  
  This will average a percentage p of the elements in the batch with other elements.
  The target will be averaged as well (this might work with binary classification
  and certain loss), following a beta distribution.
class_docstr_tok: ["Apply","SMOTE","This","will","average","a","percentage","p","of","the","elements","in","the","batch","with","other","elements",".","The","target","will","be","averaged","as","well","(","this","might","work","with","binary","classification","and","certain","loss",")",",","following","a","beta","distribution","."]
code: def _set_seed(self):
      torch.manual_seed(self.seed)
      np.random.seed(self.seed)
      return
  
file_path: tabnet/pytorch_tabnet/augmentations.py
imports: import torch
  from pytorch_tabnet.utils import define_device
  import numpy
label_desc: Use image node_id 2 for calling the RegressionSMOTE obj's underlying member method code with example usage: obj._set_seed() without return types