class_docstr: Encodes sequences with relative positional information as described in
  Section 2 of :cite:t:`https://doi.org/10.48550/arxiv.2006.11477`.
docstr: :meta private:
docstr_tok: [":","meta","private",":"]
code: def _do_forward(
      self,
      seqs: Tensor,
      padding_mask: Optional[PaddingMask],
      state_bag: Optional[IncrementalStateBag],
  ) -> Tensor:
      """:meta private:"""
      if state_bag is not None:
          raise ValueError(
              "`Wav2Vec2PositionEncoder` does not support incremental decoding."
          )
  
      # We have to ensure that the padded elements are correctly set to
      # zero; otherwise, noise will leak into the feature maps.
      seqs = apply_padding_mask(seqs, padding_mask)
  
      # (N, S, E) -> (N, E, S)
      encodings = seqs.transpose(1, 2)
  
      # (N, E, S) -> (N, E, S)
      encodings = self.conv(encodings)
  
      if self.remove_pad:
          encodings = encodings[:, :, :-1]
  
      encodings = self.activation(encodings)
  
      # (N, E, S) -> (N, S, E)
      encodings = encodings.transpose(1, 2)
  
      return seqs + encodings
  
imports: from typing import Optional, final
  import torch.nn
  from torch import Tensor
  from torch.nn import GELU, Conv1d, Module, Sequential
  from torch.nn.utils.weight_norm import remove_weight_norm, weight_norm
  from fairseq2.nn.incremental_state import IncrementalStateBag
  from fairseq2.nn.normalization import LayerNorm, StandardLayerNorm
  from fairseq2.nn.padding import PaddingMask, apply_padding_mask
  from fairseq2.nn.position_encoder import PositionEncoder
  from fairseq2.typing import DataType, Device, finaloverride, override
label_desc: Use image node_id 2 for calling the Wav2Vec2PositionEncoder obj's underlying member method code with example usage: obj._do_forward(seqs, padding_mask, state_bag) and returns: unknown