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 __init__(self, pos_encoder):
      super().__init__()
      self.pos_encoder = pos_encoder
  
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 1 to create a new TextFeatPositionalEncoder object from inherited base classes: nn with example: obj = TextFeatPositionalEncoder(pos_encoder)