code: def augmentation(data, target, **params):
      shape = data.shape
      cat_data = torch.cat([data for _ in range(params["num_augs"])])
      target = torch.cat(
          [target for _ in range(params["num_augs"])]
      ).view(-1)
      locs_to_mask = (
          torch.empty_like(cat_data, dtype=float).uniform_()
          < params["aug_mask_prob"]
      )
      cat_data[locs_to_mask] = 0
      cat_data = cat_data.view(-1, shape[-1])
      return cat_data, target
  
file_path: autogluon/tabular/src/autogluon/tabular/models/tab_transformer/utils.py
imports: import logging
  import torch
  from torch.utils.data import DataLoader, Dataset
  from .None import tab_transformer_encoder
  from .tab_transformer_encoder import NullEnc, WontEncodeError
label_desc: Use image node_id 1 for calling a global function with example usage: augmentation(data, target) and returns: cat_data,  target