code: def get_conditioning(self, conditioning_input):
      speech_conditioning_input = (
          conditioning_input.unsqueeze(1)
          if len(conditioning_input.shape) == 3
          else conditioning_input
      )
      conds = []
      for j in range(speech_conditioning_input.shape[1]):
          conds.append(
              self.contextual_embedder(speech_conditioning_input[:, j])
          )
      conds = torch.cat(conds, dim=-1)
      conds = conds.mean(dim=-1)
      return conds
  
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 3 for calling the DiffusionTts obj's underlying member method code with example usage: obj.get_conditioning(conditioning_input) and returns: conds