class_docstr: Original encoder expects (B, T) long input. This module wraps it to take
  local_encoder output which are (B, T, D) float tensors
class_docstr_tok: ["Original","encoder","expects","(","B",",","T",")","long","input",".","This","module","wraps","it","to","take","local_encoder","output","which","are","(","B",",","T",",","D",")","float","tensors"]
code: def forward(self, x, padding_mask):
      # assume padded token embeddings are 0s
      # TODO: consider using padding_mask as input
      return self.pos_encoder(x[..., 0])
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  import numpy
  from dataclasses import dataclass
  from fairseq.modules import LayerNorm, SamePad, SamePad2d, TransposeLast
label_desc: Use image node_id 2 for calling the TextFeatPositionalEncoder obj's underlying member method code with example usage: obj.forward(x, padding_mask) and returns: self