class_docstr: Lightweight Convolution assuming the input is TxBxC
  Args:
      input_size: # of channels of the input
      kernel_size: convolution channels
      padding_l: padding to the left when using "same" padding
      num_heads: number of heads used. The weight is of shape (num_heads, 1, kernel_size)
      weight_dropout: the drop rate of the DropConnect to drop the weight
      weight_softmax: normalize the weight with softmax before the convolution
      bias: use bias
  
  Shape:
      Input: TxBxC, i.e. (timesteps, batch_size, input_size)
      Output: TxBxC, i.e. (timesteps, batch_size, input_size)
  
  Attributes:
      weight: the learnable weights of the module of shape
          `(num_heads, 1, kernel_size)`
      bias:   the learnable bias of the module of shape `(input_size)`
code: def prepare_for_onnx_export_(self):
      self.onnx_trace = True
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import utils
  from fairseq.incremental_decoding_utils import with_incremental_state
  from fairseq.modules.fairseq_dropout import FairseqDropout
  from fairseq.modules.unfold import unfold1d
label_desc: Use image node_id 4 for calling the LightweightConv1dTBC obj's underlying member method code with example usage: obj.prepare_for_onnx_export_() without return types