code: def predict_full(
      model,
      model_path,
      text,
      temperature,
      topp,
      max_cfg_coef,
      min_cfg_coef,
      decoding_steps1,
      decoding_steps2,
      decoding_steps3,
      decoding_steps4,
      span_score,
      progress=gr.Progress(),
  ):
      global INTERRUPTING
      INTERRUPTING = False
      progress(0, desc="Loading model...")
      model_path = model_path.strip()
      if model_path:
          if not Path(model_path).exists():
              raise gr.Error(f"Model path {model_path} doesn't exist.")
          if not Path(model_path).is_dir():
              raise gr.Error(
                  f"Model path {model_path} must be a folder containing "
                  "state_dict.bin and compression_state_dict_.bin."
              )
          model = model_path
      if temperature < 0:
          raise gr.Error("Temperature must be >= 0.")
  
      load_model(model)
  
      max_generated = 0
  
      def _progress(generated, to_generate):
          nonlocal max_generated
          max_generated = max(generated, max_generated)
          progress((min(max_generated, to_generate), to_generate))
          if INTERRUPTING:
              raise gr.Error("Interrupted.")
  
      MODEL.set_custom_progress_callback(_progress)
  
      videos, wavs = _do_predictions(
          [text] * N_REPEATS,
          progress=True,
          temperature=temperature,
          top_p=topp,
          max_cfg_coef=max_cfg_coef,
          min_cfg_coef=min_cfg_coef,
          decoding_steps=[
              decoding_steps1,
              decoding_steps2,
              decoding_steps3,
              decoding_steps4,
          ],
          span_arrangement="stride1"
          if (span_score == PROD_STRIDE_1)
          else "nonoverlap",
          gradio_progress=progress,
      )
  
      outputs_ = [videos[0]] + [wav for wav in wavs]
      return tuple(outputs_)
  
imports: import argparse
  from concurrent.futures import ProcessPoolExecutor
  import logging
  import os
  from pathlib import Path
  import subprocess
  import sys
  from tempfile import NamedTemporaryFile
  import time
  import typing
  import warnings
  import gradio
  from audiocraft.data.audio import audio_write
  from audiocraft.models import MAGNeT
label_desc: Use image node_id 7 for calling a global function with example usage: predict_full(model, model_path, text, temperature, topp, max_cfg_coef, min_cfg_coef, decoding_steps1, decoding_steps2, decoding_steps3, decoding_steps4, span_score, progress) and returns: tuple