code: def DynamicConv(
      input_size,
      kernel_size=1,
      padding_l=None,
      num_heads=1,
      weight_dropout=0.0,
      weight_softmax=False,
      renorm_padding=False,
      bias=False,
      conv_bias=False,
      query_size=None,
      in_proj=False,
  ):
      if torch.cuda.is_available():
          try:
              from fairseq.modules.dynamicconv_layer import (
                  DynamicconvLayer,
              )
  
              return DynamicconvLayer(
                  input_size,
                  kernel_size=kernel_size,
                  padding_l=padding_l,
                  num_heads=num_heads,
                  weight_dropout=weight_dropout,
                  weight_softmax=weight_softmax,
                  renorm_padding=renorm_padding,
                  bias=bias,
                  conv_bias=conv_bias,
                  query_size=query_size,
              )
          except ImportError as e:
              print(e)
      return DynamicConv1dTBC(
          input_size,
          kernel_size=kernel_size,
          padding_l=padding_l,
          num_heads=num_heads,
          weight_dropout=weight_dropout,
          weight_softmax=weight_softmax,
          renorm_padding=renorm_padding,
          bias=bias,
          conv_bias=conv_bias,
          query_size=query_size,
      )
  
imports: from typing import Dict, Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import utils
  from fairseq.incremental_decoding_utils import FairseqIncrementalState, with_incremental_state
  from fairseq.modules.fairseq_dropout import FairseqDropout
  from torch import Tensor
  from .unfold import unfold1d
label_desc: Use image node_id 1 for calling a global function with example usage: DynamicConv(input_size, kernel_size, padding_l, num_heads, weight_dropout, weight_softmax, renorm_padding, bias, conv_bias, query_size, in_proj) and returns: DynamicConv1dTBC, DynamicconvLayer