class_docstr: Tacotron2 Postnet
  
  Args:
      in_out_channels (int): number of output channels.
  
  Shapes:
      - input: (B, C_in, T)
      - output: (B, C_in, T)
code: def __init__(self, in_out_channels, num_convs=5):
      super(Postnet, self).__init__()
      self.convolutions = nn.ModuleList()
      self.convolutions.append(
          ConvBNBlock(
              in_out_channels, 512, kernel_size=5, activation="tanh"
          )
      )
      for _ in range(1, num_convs - 1):
          self.convolutions.append(
              ConvBNBlock(512, 512, kernel_size=5, activation="tanh")
          )
      self.convolutions.append(
          ConvBNBlock(
              512, in_out_channels, kernel_size=5, activation=None
          )
      )
  
imports: import torch
  from torch import nn
  from torch.nn import functional
  from .common_layers import Prenet, Linear
  from .attentions import init_attn
label_desc: Use image node_id 1 to create a new Postnet object from inherited base classes: nn with example: obj = Postnet(in_out_channels, num_convs)