class_docstr: NN module creating a fixed size prosody embedding from a spectrogram.
  
  inputs: mel spectrograms [batch_size, num_spec_frames, num_mel]
  outputs: [batch_size, embedding_dim]
class_docstr_tok: ["NN","module","creating","a","fixed","size","prosody","embedding","from","a","spectrogram",".","inputs",":","mel","spectrograms","[","batch_size",",","num_spec_frames",",","num_mel","]","outputs",":","[","batch_size",",","embedding_dim","]"]
docstr: Height of spec after n convolutions with fixed kernel/stride/pad.
docstr_tok: ["Height","of","spec","after","n","convolutions","with","fixed","kernel\/stride\/pad","."]
code: def calculate_post_conv_height(
      height, kernel_size, stride, pad, n_convs
  ):
      """Height of spec after n convolutions with fixed kernel/stride/pad."""
      for _ in range(n_convs):
          height = (height - kernel_size + 2 * pad) // stride + 1
      return height
  
file_path: TTS/TTS/tts/layers/tacotron/gst_layers.py
imports: import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 3 for calling the ReferenceEncoder obj's underlying member method code with example usage: obj.calculate_post_conv_height(height, kernel_size, stride, pad, n_convs) and returns: height