code: def get_spec_from_most_probable_state(
      log_alpha_scaled, means, decoder=None
  ):
      """Get the most probable state means from the log_alpha_scaled.
  
      Args:
          log_alpha_scaled (torch.Tensor): Log alpha scaled values.
              - Shape: :math:`(T, N)`
          means (torch.Tensor): Means of the states.
              - Shape: :math:`(N, T, D_out)`
          decoder (torch.nn.Module): Decoder module to decode the latent to melspectrogram. Defaults to None.
      """
      max_state_numbers = torch.max(log_alpha_scaled, dim=1)[1]
      max_len = means.shape[0]
      n_mel_channels = means.shape[2]
      max_state_numbers = (
          max_state_numbers.unsqueeze(1)
          .unsqueeze(1)
          .expand(max_len, 1, n_mel_channels)
      )
      means = (
          torch.gather(means, 1, max_state_numbers)
          .squeeze(1)
          .to(log_alpha_scaled.dtype)
      )
      if decoder is not None:
          mel = (
              decoder(
                  means.T.unsqueeze(0),
                  torch.tensor([means.shape[0]], device=means.device),
                  reverse=True,
              )[0]
              .squeeze(0)
              .T
          )
      else:
          mel = means
      return mel
  
imports: from typing import Any
  import matplotlib.pyplot
  import numpy
  import torch
label_desc: Use image node_id 2 for calling a global function with example usage: get_spec_from_most_probable_state(log_alpha_scaled, means, decoder) and returns: mel