code: def infer(model, text_sequences, input_lengths):
      """
      An inference hook for pretrained synthesizers
  
      Arguments
      ---------
      model: Tacotron2
          the tacotron model
      text_sequences: torch.Tensor
          encoded text sequences
      input_lengths: torch.Tensor
          input lengths
  
      Returns
      -------
      result: tuple
          (mel_outputs_postnet, mel_lengths, alignments) - the exact
          model output
      """
      return model.infer(text_sequences, input_lengths)
  
imports: from math import sqrt
  from speechbrain.nnet.loss.guidedattn_loss import GuidedAttentionLoss
  from speechbrain.lobes.models.transformer.Transformer import get_mask_from_lengths
  import torch
  from torch import nn
  from torch.nn import functional
  from collections import namedtuple
label_desc: Use image node_id 1 for calling a global function with example usage: infer(model, text_sequences, input_lengths) and returns: model