code: def __init__(
      self,
      dropout,
      encoder_conv_dim,
      encoder_conv_type,
      self_attention_type,
      encoder_embed_dim,
      encoder_ffn_embed_dim,
      self_attention_heads,
      encoder_glu,
      encoder_normalize_before,
      input_dropout,
      relu_dropout,
      convolution_type,
      conv=None,
      self_attention=None,
  ):
      super().__init__()
      self.embed_dim = encoder_embed_dim
      self.conv_dim = encoder_conv_dim
  
      if encoder_glu:
          self.linear1 = Linear(self.embed_dim, 2 * self.conv_dim)
          self.act = nn.GLU()
      else:
          self.linear1 = Linear(self.embed_dim, self.conv_dim)
          self.act = None
      self.conv = conv
      self.linear2 = Linear(self.conv_dim, self.embed_dim)
      self.dropout = dropout
      self.relu_dropout = relu_dropout
      self.input_dropout = input_dropout
      self.normalize_before = encoder_normalize_before
      self.fc1 = Linear(self.embed_dim, encoder_ffn_embed_dim)
      self.fc2 = Linear(encoder_ffn_embed_dim, self.embed_dim)
  
      self.layer_norm1 = LayerNorm(self.embed_dim)
      self.layer_norm2 = LayerNorm(self.embed_dim)
      if self_attention is None:
          self.has_self_attn = False
          self.self_attn = PlaceholderAttentionIdentity()
      else:
          self.has_self_attn = True
          self.self_attn = self_attention
  
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 2 to create a new LightConvEncoderLayer object from inherited base classes: PyTextSeq2SeqModule with example: obj = LightConvEncoderLayer(dropout, encoder_conv_dim, encoder_conv_type, _attention_type, encoder_embed_dim, encoder_ffn_embed_dim, _attention_heads, encoder_glu, encoder_normalize_before, input_dropout, relu_dropout, convolution_type, conv, _attention)