code: def forward(self, dec_inp, mask=None, conditioning=None):
      output = self.dec_attn(
          dec_inp, attn_mask=~mask.squeeze(2), conditioning=conditioning
      )
      output *= mask
      output = self.pos_ff(output, conditioning)
      output *= mask
  
      if self.is_adapter_available():
          output = self.forward_enabled_adapters(output)
          output *= mask
  
      return output
  
imports: from typing import Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from nemo.collections.tts.modules.submodules import ConditionalInput, ConditionalLayerNorm, LinearNorm
  from nemo.collections.tts.parts.utils.helpers import get_mask_from_lengths
  from nemo.core.classes import NeuralModule, adapter_mixins, typecheck
  from nemo.core.neural_types.elements import EncodedRepresentation, LengthsType, MaskType, TokenIndex
  from nemo.core.neural_types.neural_type import NeuralType
label_desc: Use image node_id 2 for calling the TransformerLayer obj's underlying member method code with example usage: obj.forward(dec_inp, mask, conditioning) and returns: output