code: def forward(
      self,
      src_tokens: Tensor,
      src_embeddings: Tensor,
      src_lengths: Tensor,
  ) -> Dict[str, Tensor]:
      output_dict: Dict[str, Tensor] = {}
  
      # embed tokens and positions
      x = self.embed_scale * src_embeddings
      if not self.no_token_positional_embeddings:
          x = self.pos_embed(x, src_tokens)
      else:
          x = self.project_in_dim(x)
  
      x = self.embed_layer_norm(x)
      x = F.dropout(x, p=self.dropout, training=self.training)
      output_dict["encoder_layer_0"] = x.clone()
  
      # B x T x C -> T x B x C
      x = x.transpose(0, 1)
  
      # Compute padding mask
      encoder_padding_mask = src_tokens.eq(self.padding_idx)  # B x T
      if (
          not encoder_padding_mask.any()
      ):  # Setting to None helps us avoid some masking operations later.
          # Different name is used to avoid some torchscript type checks
          encoder_mask = None
      else:
          encoder_mask = encoder_padding_mask
  
      # Encoder layers
      for idx, layer in enumerate(self.layers):
          x = layer(x, encoder_mask)
          output_dict["encoder_layer_" + str(idx + 1)] = x.transpose(
              0, 1
          ).clone()
  
      if self.normalize:
          x = self.layer_norm(x)
  
      output_dict["src_tokens"] = src_tokens  # B x T
      if src_lengths is not None:
          output_dict["src_lengths"] = src_lengths
      output_dict["encoder_out"] = x  # T x B x C
      if encoder_mask is not None:
          output_dict["encoder_mask"] = encoder_mask  # B x T
      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 3 for calling the LightConvEncoder obj's underlying member method code with example usage: obj.forward(src_tokens, src_embeddings, src_lengths) and returns: output_dict