code: def reorder_encoder_out(
      self, encoder_out: Dict[str, Tensor], new_order: Tensor
  ):
      encoder = encoder_out["encoder_out"]
      encoder = encoder.index_select(1, new_order)
      output_dict = {"encoder_out": encoder}
  
      output_dict["src_tokens"] = encoder_out[
          "src_tokens"
      ].index_select(0, new_order)
      padding_mask = encoder_out.get("encoder_mask", None)
      if padding_mask is not None:
          padding_mask = padding_mask.index_select(0, new_order)
          output_dict["encoder_mask"] = padding_mask
      return output_dict
  
imports: import math
  from typing import Dict, List, Optional, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from pytext.config import ConfigBase
  from pytext.config.module_config import ModuleConfig
  from pytext.models.module import create_module
  from pytext.models.representations.transformer import PositionalEmbedding
  from pytext.models.seq_models.base import PlaceholderAttentionIdentity
  from pytext.models.seq_models.positional import PostionalEmbedCombine, PostionalEmbedType, SinusoidalPositionalEmbedding
  from torch import Tensor
  from torch.nn import LayerNorm
  from .attention import MultiheadAttention
  from .base import PlaceholderIdentity, PyTextSeq2SeqModule
  from .light_conv import LightweightConv
  from .nar_modules import NAREncoderUtility
  from .utils import Linear
label_desc: Use image node_id 4 for calling the LightConvEncoder obj's underlying member method code with example usage: obj.reorder_encoder_out(encoder_out, new_order) and returns: output_dict