code: def __init__(
      self,
      num_mels: int,
      ref_enc_filters: List[Union[int, int, int, int, int, int]],
      ref_enc_size: int,
      ref_enc_strides: List[Union[int, int, int, int, int]],
      ref_enc_gru_size: int,
      dropout: float,
      n_hidden: int,
      n_heads: int,
      bottleneck_size_p: int,
  ):
      super().__init__()
  
      self.E = n_hidden
      self.d_q = self.d_k = n_hidden
      bottleneck_size = bottleneck_size_p
  
      self.encoder = ReferenceEncoder(
          ref_enc_filters=ref_enc_filters,
          ref_enc_gru_size=ref_enc_gru_size,
          ref_enc_size=ref_enc_size,
          ref_enc_strides=ref_enc_strides,
          num_mels=num_mels,
      )
      self.encoder_prj = nn.Linear(ref_enc_gru_size, n_hidden)
      self.attention = ConformerMultiHeadedSelfAttention(
          d_model=n_hidden,
          num_heads=n_heads,
          dropout_p=dropout,
      )
      self.encoder_bottleneck = nn.Linear(n_hidden, bottleneck_size)
  
imports: from typing import List, Tuple, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from TTS.tts.layers.delightful_tts.conformer import ConformerMultiHeadedSelfAttention
  from TTS.tts.layers.delightful_tts.conv_layers import CoordConv1d
  from TTS.tts.layers.delightful_tts.networks import STL
label_desc: Use image node_id 1 to create a new PhonemeLevelProsodyEncoder object from inherited base classes: nn with example: obj = PhonemeLevelProsodyEncoder(num_mels, ref_enc_filters, ref_enc_size, ref_enc_strides, ref_enc_gru_size, dropout, n_hidden, n_heads, bottleneck_size_p)