docstr: Args:
      x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`
      encoder_padding_mask (ByteTensor): binary ByteTensor of shape
          `(batch, src_len)` where padding elements are indicated by ``1``.
  
  Returns:
      encoded output of shape `(batch, src_len, embed_dim)`
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code: def forward(self, x, encoder_padding_mask: Optional[Tensor] = None):
      """
      Args:
          x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`
          encoder_padding_mask (ByteTensor): binary ByteTensor of shape
              `(batch, src_len)` where padding elements are indicated by ``1``.
  
      Returns:
          encoded output of shape `(batch, src_len, embed_dim)`
      """
      residual = x
  
      normalize = self.maybe_layer_norm(before=True)
      if normalize:
          x = self.layer_norm1(x)
      if self.has_self_attn:
          x, _ = self.self_attn(
              query=x,
              key=x,
              value=x,
              key_padding_mask=encoder_padding_mask,
              incremental_state=None,
              need_weights=False,
          )
          x = residual + x
          residual = x
      x = F.dropout(x, p=self.input_dropout, training=self.training)
      x = self.linear1(x)
      x = self.act(x)
      if encoder_padding_mask is not None:
          x = x.masked_fill(
              encoder_padding_mask.transpose(0, 1).unsqueeze(2), 0
          )
      x = self.conv(x)
      x = self.linear2(x)
      x = F.dropout(x, p=self.dropout, training=self.training)
      x = residual + x
  
      normalize = self.maybe_layer_norm(after=True)
      if normalize:
          x = self.layer_norm1(x)
  
      residual = x
      normalize = self.maybe_layer_norm(before=True)
      if normalize:
          x = self.layer_norm2(x)
      x = F.relu(self.fc1(x))
      x = F.dropout(x, p=self.relu_dropout, training=self.training)
      x = self.fc2(x)
      x = F.dropout(x, p=self.dropout, training=self.training)
      x = residual + x
  
      normalize = self.maybe_layer_norm(after=True)
      if normalize:
          x = self.layer_norm2(x)
      return x
  
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 LightConvEncoderLayer obj's underlying member method code with example usage: obj.forward(x, encoder_padding_mask) and returns: x