code: def forward(self, inputs, cond=None):
      if self.conditional:
          if self.hidden_units is not None:
              cond = self.hidden_dense(cond)
  
          for _ in range(len(inputs.shape) - len(cond.shape)):
              cond = cond.unsqueeze(1)  # cond = K.expand_dims(cond, 1)
  
          if self.center:
              beta = self.beta_dense(cond) + self.beta
          if self.scale:
              gamma = self.gamma_dense(cond) + self.gamma
      else:
          if self.center:
              beta = self.beta
          if self.scale:
              gamma = self.gamma
  
      outputs = inputs
      if self.center:
          mean = torch.mean(outputs, dim=-1).unsqueeze(-1)
          outputs = outputs - mean
      if self.scale:
          variance = torch.mean(outputs**2, dim=-1).unsqueeze(-1)
          std = (variance + self.epsilon) ** 0.5
          outputs = outputs / std
          outputs = outputs * gamma
      if self.center:
          outputs = outputs + beta
  
      return outputs
  
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 3 for calling the LayerNorm obj's underlying member method code with example usage: obj.forward(inputs, cond) and returns: outputs