class_docstr: Multi-Period (MPD) Discriminator.
  
  Args:
      in_channels (int): Number of input channels.
      out_channels (int): Number of output channels.
      periods (Sequence[int]): Periods between samples of audio for the sub-discriminators.
      **kwargs: Additional args for `PeriodDiscriminator`
class_docstr_tok: ["Multi-Period","(","MPD",")","Discriminator",".","Args",":","in_channels","(","int",")",":","Number","of","input","channels",".","out_channels","(","int",")",":","Number","of","output","channels",".","periods","(","Sequence","[","int","]",")",":","Periods","between","samples","of","audio","for","the","sub-discriminators",".","*","*","kwargs",":","Additional","args","for","`","PeriodDiscriminator","`"]
code: def __init__(
      self,
      in_channels: int = 1,
      out_channels: int = 1,
      periods: tp.Sequence[int] = [2, 3, 5, 7, 11],
      **kwargs,
  ):
      super().__init__()
      self.discriminators = nn.ModuleList(
          [
              PeriodDiscriminator(
                  p, in_channels, out_channels, **kwargs
              )
              for p in periods
          ]
      )
  
file_path: audiocraft/audiocraft/adversarial/discriminators/mpd.py
imports: import typing
  import torch
  import torch.nn
  import torch.nn.functional
  from ...modules import NormConv2d
  from .base import MultiDiscriminator, MultiDiscriminatorOutputType
label_desc: Use image node_id 1 to create a new MultiPeriodDiscriminator object from inherited base classes: MultiDiscriminator with example: obj = MultiPeriodDiscriminator(in_channels, out_channels, periods)