code: def from_config(cls, config, kernel_size):
      conv = create_module(
          config.encoder_conv_type,
          input_size=config.encoder_conv_dim,
          kernel_size=kernel_size,
          convolution_type="non-causal",
      )
      if config.self_attention_type is not None:
          self_attention = create_module(
              config.self_attention_type,
              config.encoder_embed_dim,
              config.self_attention_heads,
          )
      else:
          self_attention = None
      return cls(
          **config._asdict(), conv=conv, self_attention=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 1 for calling the LightConvEncoderLayer obj's underlying member method code with example usage: obj.from_config(cls, config, kernel_size) and returns: cls