code: def _forward(self, inp, conditioning=None):
      if self.pre_lnorm:
          # layer normalization + positionwise feed-forward
          core_out = inp.transpose(1, 2)
          core_out = self.CoreNet(
              self.layer_norm(core_out, conditioning).to(inp.dtype)
          )
          core_out = core_out.transpose(1, 2)
  
          # residual connection
          output = core_out + inp
      else:
          # positionwise feed-forward
          core_out = inp.transpose(1, 2)
          core_out = self.CoreNet(core_out)
          core_out = core_out.transpose(1, 2)
  
          # residual connection + layer normalization
          output = self.layer_norm(inp + core_out, conditioning).to(
              inp.dtype
          )
  
      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 3 for calling the PositionwiseConvFF obj's underlying member method code with example usage: obj._forward(inp, conditioning) and returns: output