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)
class_docstr_tok: ["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",")"]
code: def init_layers(self):
      if isinstance(self.activation, torch.nn.ReLU):
          w_gain = "relu"
      elif isinstance(self.activation, torch.nn.Tanh):
          w_gain = "tanh"
      elif self.activation is None:
          w_gain = "linear"
      else:
          raise RuntimeError("Unknown activation function")
      torch.nn.init.xavier_uniform_(
          self.conv1d.weight, gain=torch.nn.init.calculate_gain(w_gain)
      )
  
imports: import torch
  from torch import nn
  from .common_layers import Prenet
  from .attentions import init_attn
label_desc: Use image node_id 2 for calling the BatchNormConv1d obj's underlying member method code with example usage: obj.init_layers() without return types