code: def __init__(
      self,
      n_tokens,
      emb_dim,
      channels,
      kernel,
      dropout,
      n_layers,
      spk_emb,
      gst_emb,
      n_bins,
      f0_pred,
      f0_log,
      f0_norm,
  ):
      super(CnnPredictor, self).__init__()
      self.n_tokens = n_tokens
      self.emb_dim = emb_dim
      self.f0_log = f0_log
      self.f0_pred = f0_pred
      self.padding_token = n_tokens
      self.f0_norm = f0_norm
      # add 1 extra embedding for padding token, set the padding index to be the last token
      # (tokens from the clustering start at index 0)
      self.token_emb = nn.Embedding(
          n_tokens + 1, emb_dim, padding_idx=self.padding_token
      )
  
      self.spk_emb = spk_emb
      self.gst_emb = nn.Embedding(20, gst_emb)
      self.setup = False
  
      feats = emb_dim + gst_emb
      # feats = emb_dim + gst_emb + (256 if spk_emb else 0)
      layers = [
          nn.Sequential(
              Rearrange("b t c -> b c t"),
              nn.Conv1d(
                  feats,
                  channels,
                  kernel_size=kernel,
                  padding=(kernel - 1) // 2,
              ),
              Rearrange("b c t -> b t c"),
              nn.ReLU(),
              nn.LayerNorm(channels),
              nn.Dropout(dropout),
          )
      ]
      for _ in range(n_layers - 1):
          layers += [
              nn.Sequential(
                  Rearrange("b t c -> b c t"),
                  nn.Conv1d(
                      channels,
                      channels,
                      kernel_size=kernel,
                      padding=(kernel - 1) // 2,
                  ),
                  Rearrange("b c t -> b t c"),
                  nn.ReLU(),
                  nn.LayerNorm(channels),
                  nn.Dropout(dropout),
              )
          ]
      self.conv_layer = nn.ModuleList(layers)
      self.proj = nn.Linear(channels, n_bins)
  
imports: import logging
  import os
  import random
  import sys
  from collections import defaultdict
  import hydra
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from einops import rearrange
  from einops.layers.torch import Rearrange
  from scipy.io.wavfile import read
  from scipy.ndimage import gaussian_filter1d
  from torch.utils.data import DataLoader, Dataset
  from tqdm import tqdm
  from dataset import parse_speaker, parse_style
  from .utils import F0Stat
label_desc: Use image node_id 1 to create a new CnnPredictor object from inherited base classes: nn with example: obj = CnnPredictor(n_tokens, emb_dim, channels, kernel, dropout, n_layers, spk_emb, gst_emb, n_bins, f0_pred, f0_log, f0_norm)