class_docstr: Dynamic lightweight convolution taking T x B x C inputs
  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
      renorm_padding: re-normalize the filters to ignore the padded part (only the non-padding parts sum up to 1)
      bias: use bias
      conv_bias: bias of the convolution
      query_size: specified when feeding a different input as the query
      in_proj: project the input and generate the filter together
  
  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)`
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docstr: Assuming the input, x, of the shape T x B x C and producing an output in the shape T x B x C
  args:
      x: Input of shape T x B x C, i.e. (timesteps, batch_size, input_size)
      incremental_state: A dict to keep the state
      unfold: unfold the input or not. If not, we use the matrix trick instead
      query: use the specified query to predict the conv filters
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code: def forward(
      self,
      x,
      incremental_state: Optional[
          Dict[str, Dict[str, Optional[Tensor]]]
      ] = None,
      query: Optional[Tensor] = None,
  ):
      """Assuming the input, x, of the shape T x B x C and producing an output in the shape T x B x C
      args:
          x: Input of shape T x B x C, i.e. (timesteps, batch_size, input_size)
          incremental_state: A dict to keep the state
          unfold: unfold the input or not. If not, we use the matrix trick instead
          query: use the specified query to predict the conv filters
      """
      assert query is None or not self.in_proj
  
      if query is None:
          query = x
  
      output = self._forward_unfolded(x, incremental_state, query)
  
      if self.has_conv_bias:
          output = output + self.conv_bias
      return output
  
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 3 for calling the DynamicConv_scripatable obj's underlying member method code with example usage: obj.forward(x, incremental_state, query) and returns: output