code: def get_logits(
      self,
      first_inputs,
      first_head,
      second_inputs=None,
      second_head=None,
      prompt=None,
      get_attns=False,
      return_latent=False,
      attn_mask_cond=None,
      attn_mask_text=None,
      attn_mask_mel=None,
  ):
      if prompt is not None:
          offset = prompt.shape[1]
          if second_inputs is not None:
              emb = torch.cat(
                  [prompt, first_inputs, second_inputs], dim=1
              )
          else:
              emb = torch.cat([prompt, first_inputs], dim=1)
  
      # with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
      attn_mask = None
      if attn_mask_text is not None:
          attn_mask = torch.cat([attn_mask_text, attn_mask_mel], dim=1)
          if prompt is not None:
              attn_mask_cond = torch.ones(
                  prompt.shape[0],
                  offset,
                  dtype=torch.bool,
                  device=emb.device,
              )
              attn_mask = torch.cat([attn_mask_cond, attn_mask], dim=1)
  
      gpt_out = self.gpt(
          inputs_embeds=emb,
          return_dict=True,
          output_attentions=get_attns,
          attention_mask=attn_mask,
      )
  
      if get_attns:
          return gpt_out.attentions
  
      enc = gpt_out.last_hidden_state[:, offset:]
      enc = self.final_norm(enc)
  
      if return_latent:
          return (
              enc[:, : first_inputs.shape[1]],
              enc[:, -second_inputs.shape[1] :],
          )
  
      first_logits = enc[:, : first_inputs.shape[1]]
      first_logits = first_head(first_logits)
      first_logits = first_logits.permute(0, 2, 1)
      if second_inputs is not None:
          second_logits = enc[:, -second_inputs.shape[1] :]
          second_logits = second_head(second_logits)
          second_logits = second_logits.permute(0, 2, 1)
          return first_logits, second_logits
      else:
          return first_logits
  
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 6 for calling the GPT obj's underlying member method code with example usage: obj.get_logits(first_inputs, first_head, second_inputs, second_head, prompt, get_attns, return_latent, attn_mask_cond, attn_mask_text, attn_mask_mel) and returns: gpt_out, enc,  enc, first_logits,  second_logits, first_logits