code: def __init__(
      self,
      input_dim,
      output_dim,
      shared_layers,
      n_glu_independent,
      virtual_batch_size=128,
      momentum=0.02,
  ):
      super(FeatTransformer, self).__init__()
      """
          Initialize a feature transformer.
  
          Parameters
          ----------
          input_dim : int
              Input size
          output_dim : int
              Output_size
          shared_layers : torch.nn.ModuleList
              The shared block that should be common to every step
          n_glu_independent : int
              Number of independent GLU layers
          virtual_batch_size : int
              Batch size for Ghost Batch Normalization within GLU block(s)
          momentum : float
              Float value between 0 and 1 which will be used for momentum in batch norm
          """
  
      params = {
          "n_glu": n_glu_independent,
          "virtual_batch_size": virtual_batch_size,
          "momentum": momentum,
      }
  
      if shared_layers is None:
          # no shared layers
          self.shared = torch.nn.Identity()
          is_first = True
      else:
          self.shared = GLU_Block(
              input_dim,
              output_dim,
              first=True,
              shared_layers=shared_layers,
              n_glu=len(shared_layers),
              virtual_batch_size=virtual_batch_size,
              momentum=momentum,
          )
          is_first = False
  
      if n_glu_independent == 0:
          # no independent layers
          self.specifics = torch.nn.Identity()
      else:
          spec_input_dim = input_dim if is_first else output_dim
          self.specifics = GLU_Block(
              spec_input_dim, output_dim, first=is_first, **params
          )
  
imports: import torch
  from torch.nn import Linear, BatchNorm1d, ReLU
  import numpy
  from pytorch_tabnet import sparsemax
label_desc: Use image node_id 1 to create a new FeatTransformer object from inherited base classes: torch.nn with example: obj = FeatTransformer(input_dim, output_dim, shared_layers, n_glu_independent, virtual_batch_size, momentum)