class_docstr: Zero-pads model inputs and targets based on number of frames per step
  
  Arguments
  ---------
  n_frames_per_step: int
      the number of output frames per step
  
  Returns
  -------
  result: tuple
      a tuple of tensors to be used as inputs/targets
      (
          text_padded,
          input_lengths,
          mel_padded,
          gate_padded,
          output_lengths,
          len_x
      )
class_docstr_tok: ["Zero-pads","model","inputs","and","targets","based","on","number","of","frames","per","step","Arguments","--","--","--","--","-","n_frames_per_step",":","int","the","number","of","output","frames","per","step","Returns","--","--","--","-","result",":","tuple","a","tuple","of","tensors","to","be","used","as","inputs\/targets","(","text_padded",",","input_lengths",",","mel_padded",",","gate_padded",",","output_lengths",",","len_x",")"]
code: def __init__(self, n_frames_per_step=1):
      self.n_frames_per_step = n_frames_per_step
  
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 to create a new TextMelCollate object  with example: obj = TextMelCollate(n_frames_per_step)