code: def __init__(
      self,
      input_dim,
      cond_dim=0,
      center=True,
      scale=True,
      epsilon=None,
      conditional=False,
      hidden_units=None,
      hidden_activation="linear",
      hidden_initializer="xaiver",
      **kwargs,
  ):
      super(LayerNorm, self).__init__()
      """
          input_dim: inputs.shape[-1]
          cond_dim: cond.shape[-1]
          """
      self.center = center
      self.scale = scale
      self.conditional = conditional
      self.hidden_units = hidden_units
      self.hidden_initializer = hidden_initializer
      self.epsilon = epsilon or 1e-12
      self.input_dim = input_dim
      self.cond_dim = cond_dim
  
      if self.center:
          self.beta = nn.Parameter(torch.zeros(input_dim))
      if self.scale:
          self.gamma = nn.Parameter(torch.ones(input_dim))
  
      if self.conditional:
          if self.hidden_units is not None:
              self.hidden_dense = nn.Linear(
                  in_features=self.cond_dim,
                  out_features=self.hidden_units,
                  bias=False,
              )
          if self.center:
              self.beta_dense = nn.Linear(
                  in_features=self.cond_dim,
                  out_features=input_dim,
                  bias=False,
              )
          if self.scale:
              self.gamma_dense = nn.Linear(
                  in_features=self.cond_dim,
                  out_features=input_dim,
                  bias=False,
              )
  
      self.initialize_weights()
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
  from transformers import AutoModel
label_desc: Use image node_id 1 to create a new LayerNorm object from inherited base classes: nn with example: obj = LayerNorm(input_dim, cond_dim, center, scale, epsilon, conditional, hidden_units, hidden_activation, hidden_initializer)