class_docstr: Module to get pitch embeddings via pitch predictor
  
  Args:
      n_input (int): Number of pitch predictor input channels.
      n_hidden (int): Number of pitch predictor hidden channels.
      n_out (int): Number of pitch predictor out channels.
      kernel size (int): Size of the kernel for conv layers.
      emb_kernel_size (int): Size the kernel for the pitch embedding.
      p_dropout (float): Probability of dropout.
      lrelu_slope (float): Slope for the leaky relu.
  
  Inputs: inputs, mask
      - **inputs** (batch, time1, dim): Tensor containing input vector
      - **target** (batch, 1, time2): Tensor containing the pitch target
      - **dr** (batch, time1): Tensor containing aligner durations vector
      - **mask** (batch, time1): Tensor containing indices to be masked
  Returns:
      - **pitch prediction** (batch, 1, time1): Tensor produced by pitch predictor
      - **pitch embedding** (batch, channels, time1): Tensor produced pitch pitch adaptor
      - **average pitch target(train only)** (batch, 1, time1): Tensor produced after averaging over durations
code: def get_pitch_embedding(
      self,
      x: torch.Tensor,
      mask: torch.Tensor,
      pitch_transform: Callable,
      pitch_mean: torch.Tensor,
      pitch_std: torch.Tensor,
  ) -> torch.Tensor:
      pitch_pred = self.pitch_predictor(x, mask)
      if pitch_transform is not None:
          pitch_pred = pitch_transform(
              pitch_pred, (~mask).sum(), pitch_mean, pitch_std
          )
      pitch_pred.unsqueeze_(1)
      pitch_emb_pred = self.pitch_emb(pitch_pred)
      return pitch_emb_pred, pitch_pred
  
file_path: TTS/TTS/tts/layers/delightful_tts/pitch_adaptor.py
imports: from typing import Callable, Tuple
  import torch
  import torch.nn
  from TTS.tts.layers.delightful_tts.variance_predictor import VariancePredictor
  from TTS.tts.utils.helpers import average_over_durations
label_desc: Use image node_id 3 for calling the PitchAdaptor obj's underlying member method code with example usage: obj.get_pitch_embedding(x, mask, pitch_transform, pitch_mean, pitch_std) and returns: pitch_emb_pred,  pitch_pred