code: def get_conditioning(self, speech_conditioning_input):
      speech_conditioning_input = (
          speech_conditioning_input.unsqueeze(1)
          if len(speech_conditioning_input.shape) == 3
          else speech_conditioning_input
      )
      conds = []
      for j in range(speech_conditioning_input.shape[1]):
          conds.append(
              self.conditioning_encoder(speech_conditioning_input[:, j])
          )
      conds = torch.stack(conds, dim=1)
      conds = conds.mean(dim=1)
      return conds
  
imports: import functools
  import math
  import random
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers import GPT2Config
  from TTS.tts.layers.xtts.gpt_inference import GPT2InferenceModel
  from TTS.tts.layers.xtts.latent_encoder import ConditioningEncoder
  from TTS.tts.layers.xtts.perceiver_encoder import PerceiverResampler
label_desc: Use image node_id 7 for calling the GPT obj's underlying member method code with example usage: obj.get_conditioning(speech_conditioning_input) and returns: conds