code: def _linear_dropout_bn(
      activation,
      initialization,
      use_batch_norm,
      in_units,
      out_units,
      dropout,
  ):
      if isinstance(activation, str):
          _activation = getattr(nn, activation)
      else:
          _activation = activation
      layers = []
      if use_batch_norm:
          from pytorch_tabular.models.common.layers.batch_norm import (
              BatchNorm1d,
          )
  
          layers.append(BatchNorm1d(num_features=in_units))
      linear = nn.Linear(in_units, out_units)
      _initialize_layers(activation, initialization, linear)
      layers.extend([linear, _activation()])
      if dropout != 0:
          layers.append(nn.Dropout(dropout))
      return layers
  
file_path: pytorch_tabular/src/pytorch_tabular/utils/nn_utils.py
imports: import torch
  import torch.nn
  from .logger import get_logger
label_desc: Use image node_id 2 for calling a global function with example usage: _linear_dropout_bn(activation, initialization, use_batch_norm, in_units, out_units, dropout) and returns: layers