docstr: Prepares decoder inputs, i.e. mel outputs
  PARAMS
  ------
  decoder_inputs: inputs used for teacher-forced training, i.e. mel-specs
  
  RETURNS
  -------
  inputs: processed decoder inputs
docstr_tok: ["Prepares","decoder","inputs",",","i.e",".","mel","outputs","PARAMS","--","--","--","decoder_inputs",":","inputs","used","for","teacher-forced","training",",","i.e",".","mel-specs","RETURNS","--","--","--","-","inputs",":","processed","decoder","inputs"]
code: def parse_decoder_inputs(self, decoder_inputs):
      """Prepares decoder inputs, i.e. mel outputs
      PARAMS
      ------
      decoder_inputs: inputs used for teacher-forced training, i.e. mel-specs
  
      RETURNS
      -------
      inputs: processed decoder inputs
  
      """
      # (B, n_mel_channels, T_out) -> (B, T_out, n_mel_channels)
      decoder_inputs = decoder_inputs.transpose(1, 2)
      decoder_inputs = decoder_inputs.view(
          decoder_inputs.size(0),
          int(decoder_inputs.size(1) / self.n_frames_per_step),
          -1,
      )
      # (B, T_out, n_mel_channels) -> (T_out, B, n_mel_channels)
      decoder_inputs = decoder_inputs.transpose(0, 1)
      return decoder_inputs
  
imports: from math import sqrt
  import torch
  import torch.distributions
  from torch.autograd import Variable
  from torch import nn
  from torch.nn import functional
  from .layers import ConvNorm, LinearNorm, GlobalAvgPool
  from .utils import to_gpu, get_mask_from_lengths
label_desc: Use image node_id 4 for calling the Decoder obj's underlying member method code with example usage: obj.parse_decoder_inputs(decoder_inputs) and returns: decoder_inputs