class_docstr: A wrapper for Conv1d with BatchNorm. It sets the activation
  function between Conv and BatchNorm layers. BatchNorm layer
  is initialized with the TF default values for momentum and eps.
  
  Args:
      in_channels: size of each input sample
      out_channels: size of each output samples
      kernel_size: kernel size of conv filters
      stride: stride of conv filters
      padding: padding of conv filters
      activation: activation function set b/w Conv1d and BatchNorm
  
  Shapes:
      - input: (B, D)
      - output: (B, D)
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code: def __init__(
      self,
      in_channels,
      out_channels,
      kernel_size,
      stride,
      padding,
      activation=None,
  ):
      super().__init__()
      self.padding = padding
      self.padder = nn.ConstantPad1d(padding, 0)
      self.conv1d = nn.Conv1d(
          in_channels,
          out_channels,
          kernel_size=kernel_size,
          stride=stride,
          padding=0,
          bias=False,
      )
      # Following tensorflow's default parameters
      self.bn = nn.BatchNorm1d(out_channels, momentum=0.99, eps=1e-3)
      self.activation = activation
  
imports: import torch
  from torch import nn
  from .attentions import init_attn
  from .common_layers import Prenet
label_desc: Use image node_id 1 to create a new BatchNormConv1d object from inherited base classes: nn with example: obj = BatchNormConv1d(in_channels, out_channels, kernel_size, stride, padding, activation)