code: def timestep_independent(
      self,
      aligned_conditioning,
      conditioning_latent,
      expected_seq_len,
      return_code_pred,
  ):
      # Shuffle aligned_latent to BxCxS format
      if is_latent(aligned_conditioning):
          aligned_conditioning = aligned_conditioning.permute(0, 2, 1)
  
      cond_scale, cond_shift = torch.chunk(
          conditioning_latent, 2, dim=1
      )
      if is_latent(aligned_conditioning):
          code_emb = self.latent_conditioner(aligned_conditioning)
      else:
          code_emb = self.code_embedding(aligned_conditioning).permute(
              0, 2, 1
          )
          code_emb = self.code_converter(code_emb)
      code_emb = self.code_norm(code_emb) * (
          1 + cond_scale.unsqueeze(-1)
      ) + cond_shift.unsqueeze(-1)
  
      unconditioned_batches = torch.zeros(
          (code_emb.shape[0], 1, 1), device=code_emb.device
      )
      # Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance.
      if self.training and self.unconditioned_percentage > 0:
          unconditioned_batches = (
              torch.rand(
                  (code_emb.shape[0], 1, 1), device=code_emb.device
              )
              < self.unconditioned_percentage
          )
          code_emb = torch.where(
              unconditioned_batches,
              self.unconditioned_embedding.repeat(
                  aligned_conditioning.shape[0], 1, 1
              ),
              code_emb,
          )
      expanded_code_emb = F.interpolate(
          code_emb, size=expected_seq_len, mode="nearest"
      )
  
      if not return_code_pred:
          return expanded_code_emb
      else:
          mel_pred = self.mel_head(expanded_code_emb)
          # Multiply mel_pred by !unconditioned_branches, which drops the gradient on unconditioned branches. This is because we don't want that gradient being used to train parameters through the codes_embedder as it unbalances contributions to that network from the MSE loss.
          mel_pred = mel_pred * unconditioned_batches.logical_not()
          return expanded_code_emb, mel_pred
  
imports: import math
  import random
  from abc import abstractmethod
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import autocast
  from TTS.tts.layers.tortoise.arch_utils import AttentionBlock, normalization
label_desc: Use image node_id 4 for calling the DiffusionTts obj's underlying member method code with example usage: obj.timestep_independent(aligned_conditioning, conditioning_latent, expected_seq_len, return_code_pred) and returns: expanded_code_emb, expanded_code_emb,  mel_pred