code: def inference_speech(
      self,
      speech_conditioning_latent,
      text_inputs,
      input_tokens=None,
      num_return_sequences=1,
      max_generate_length=None,
      typical_sampling=False,
      typical_mass=0.9,
      **hf_generate_kwargs,
  ):
      text_inputs = F.pad(
          text_inputs, (0, 1), value=self.stop_text_token
      )
      text_inputs, text_targets = self.build_aligned_inputs_and_targets(
          text_inputs, self.start_text_token, self.stop_text_token
      )
      text_emb = self.text_embedding(
          text_inputs
      ) + self.text_pos_embedding(text_inputs)
  
      conds = speech_conditioning_latent.unsqueeze(1)
      emb = torch.cat([conds, text_emb], dim=1)
      self.inference_model.store_mel_emb(emb)
  
      fake_inputs = torch.full(
          (
              emb.shape[0],
              conds.shape[1] + emb.shape[1],
          ),
          fill_value=1,
          dtype=torch.long,
          device=text_inputs.device,
      )
      fake_inputs[:, -1] = self.start_mel_token
      trunc_index = fake_inputs.shape[1]
      if input_tokens is None:
          inputs = fake_inputs
      else:
          assert (
              num_return_sequences % input_tokens.shape[0] == 0
          ), "The number of return sequences must be divisible by the number of input sequences"
          fake_inputs = fake_inputs.repeat(num_return_sequences, 1)
          input_tokens = input_tokens.repeat(
              num_return_sequences // input_tokens.shape[0], 1
          )
          inputs = torch.cat([fake_inputs, input_tokens], dim=1)
  
      logits_processor = (
          LogitsProcessorList([TypicalLogitsWarper(mass=typical_mass)])
          if typical_sampling
          else LogitsProcessorList()
      )  # TODO disable this
      max_length = (
          trunc_index + self.max_mel_tokens - 1
          if max_generate_length is None
          else trunc_index + max_generate_length
      )
      gen = self.inference_model.generate(
          inputs,
          bos_token_id=self.start_mel_token,
          pad_token_id=self.stop_mel_token,
          eos_token_id=self.stop_mel_token,
          max_length=max_length,
          logits_processor=logits_processor,
          num_return_sequences=num_return_sequences,
          **hf_generate_kwargs,
      )
      return gen[:, trunc_index:]
  
imports: import functools
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers import GPT2Config, GPT2PreTrainedModel, LogitsProcessorList
  from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
  from TTS.tts.layers.tortoise.arch_utils import AttentionBlock, TypicalLogitsWarper
label_desc: Use image node_id 8 for calling the UnifiedVoice obj's underlying member method code with example usage: obj.inference_speech(speech_conditioning_latent, text_inputs, input_tokens, num_return_sequences, max_generate_length, typical_sampling, typical_mass) and returns: gen