class_docstr: Variance Adaptor with an added 1D conv layer. Used to
  get energy embeddings.
  
  Args:
      channels_in (int): Number of in channels for conv layers.
      channels_out (int): Number of out channels.
      kernel_size (int): Size the kernel for the conv layers.
      dropout (float): Probability of dropout.
      lrelu_slope (float): Slope for the leaky relu.
      emb_kernel_size (int): Size the kernel for the pitch embedding.
  
  Inputs: inputs, mask
      - **inputs** (batch, time1, dim): Tensor containing input vector
      - **target** (batch, 1, time2): Tensor containing the energy target
      - **dr** (batch, time1): Tensor containing aligner durations vector
      - **mask** (batch, time1): Tensor containing indices to be masked
  Returns:
      - **energy prediction** (batch, 1, time1): Tensor produced by energy predictor
      - **energy embedding** (batch, channels, time1): Tensor produced energy adaptor
      - **average energy target(train only)** (batch, 1, time1): Tensor produced after averaging over durations
code: def get_energy_embedding(
      self,
      x: torch.Tensor,
      mask: torch.Tensor,
      energy_transform: Callable,
  ) -> torch.Tensor:
      energy_pred = self.energy_predictor(x, mask)
      energy_pred.unsqueeze_(1)
      if energy_transform is not None:
          energy_pred = energy_transform(
              energy_pred,
              (~mask).sum(dim=(1, 2)),
              self.pitch_mean,
              self.pitch_std,
          )
      energy_emb_pred = self.energy_emb(energy_pred)
      return energy_emb_pred, energy_pred
  
file_path: TTS/TTS/tts/layers/delightful_tts/energy_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 EnergyAdaptor obj's underlying member method code with example usage: obj.get_energy_embedding(x, mask, energy_transform) and returns: energy_emb_pred,  energy_pred