class_docstr: Referance encoder for utterance and phoneme prosody encoders. Reference encoder
  made up of convolution and RNN layers.
  
  Args:
      num_mels (int): Number of mel frames to produce.
      ref_enc_filters (list[int]): List of channel sizes for encoder layers.
      ref_enc_size (int): Size of the kernel for the conv layers.
      ref_enc_strides (List[int]): List of strides to use for conv layers.
      ref_enc_gru_size (int): Number of hidden features for the gated recurrent unit.
  
  Inputs: inputs, mask
      - **inputs** (batch, dim, time): Tensor containing mel vector
      - **lengths** (batch): Tensor containing the mel lengths.
  Returns:
      - **outputs** (batch, time, dim): Tensor produced by Reference Encoder.
code: def calculate_channels(  # pylint: disable=no-self-use
      self,
      L: int,
      kernel_size: int,
      stride: int,
      pad: int,
      n_convs: int,
  ) -> int:
      for _ in range(n_convs):
          L = (L - kernel_size + 2 * pad) // stride + 1
      return L
  
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 3 for calling the ReferenceEncoder obj's underlying member method code with example usage: obj.calculate_channels(L, kernel_size, stride, pad, n_convs) and returns: L