class_docstr: Time Delayed Neural Module (TDNN) - 1D
  input:
      inp_filters: input filter channels for conv layer
      out_filters: output filter channels for conv layer
      kernel_size: kernel weight size for conv layer
      dilation: dilation for conv layer
      stride: stride for conv layer
      padding: padding for conv layer (default None: chooses padding value such that input and output feature shape matches)
  output:
      tdnn layer output 
class_docstr_tok: ["Time","Delayed","Neural","Module","(","TDNN",")","-","1D","input",":","inp_filters",":","input","filter","channels","for","conv","layer","out_filters",":","output","filter","channels","for","conv","layer","kernel_size",":","kernel","weight","size","for","conv","layer","dilation",":","dilation","for","conv","layer","stride",":","stride","for","conv","layer","padding",":","padding","for","conv","layer","(","default","None",":","chooses","padding","value","such","that","input","and","output","feature","shape","matches",")","output",":","tdnn","layer","output"]
code: def __init__(
      self,
      inp_filters: int,
      out_filters: int,
      kernel_size: int = 1,
      dilation: int = 1,
      stride: int = 1,
      padding: int = None,
  ):
      super().__init__()
      if padding is None:
          padding = get_same_padding(
              kernel_size, stride=stride, dilation=dilation
          )
  
      self.conv_layer = nn.Conv1d(
          in_channels=inp_filters,
          out_channels=out_filters,
          kernel_size=kernel_size,
          dilation=dilation,
          padding=padding,
      )
  
      self.activation = nn.ReLU()
      self.bn = nn.BatchNorm1d(out_filters)
  
imports: from typing import List
  import torch
  from numpy import inf
  from torch import nn
  from torch.nn import functional
  from nemo.collections.asr.parts.submodules.jasper import get_same_padding, init_weights
label_desc: Use image node_id 1 to create a new TDNNModule object from inherited base classes: nn with example: obj = TDNNModule(inp_filters, out_filters, kernel_size, dilation, stride, padding)