code: def __init__(
      self,
      config: "OnnxConfig",
      model: Union["PreTrainedModel", "TFPreTrainedModel"],
      model_kwargs: Dict[str, Any],
  ):
      super().__init__(config, model, model_kwargs)
  
      self.original_speecht5_prenet_forward = (
          model.speecht5.decoder.prenet.forward
      )
  
      model.vocoder = model_kwargs["vocoder_model"].eval()
  
      def patched_forward(
          input_ids=None,
          speaker_embeddings=None,
          encoder_outputs=None,
          past_key_values=None,
          output_sequence=None,
          spectrogram=None,
          encoder_attention_mask=None,
      ):
          use_cache = (
              self.real_config.use_past
              and self.real_config.variant == "with-past"
          )
          if self.real_config._behavior == "encoder":
              encoder_attention_mask = torch.ones_like(input_ids)
  
              encoder_out = model.speecht5.encoder(
                  input_values=input_ids,
                  attention_mask=encoder_attention_mask,
                  return_dict=True,
              )
              # downsample encoder attention mask
              if isinstance(
                  model.speecht5.encoder,
                  SpeechT5EncoderWithSpeechPrenet,
              ):
                  encoder_attention_mask = model.speecht5.encoder.prenet._get_feature_vector_attention_mask(
                      encoder_out[0].shape[1], encoder_attention_mask
                  )
  
              result = {
                  "encoder_outputs": encoder_out.last_hidden_state,
                  "encoder_attention_mask": encoder_attention_mask,
              }
  
          elif self.real_config._behavior == "decoder":
              # TODO: and self.real_config.use_past_in_inputs
              encoder_hidden_states = encoder_outputs[0]
  
              decoder_hidden_states = model.speecht5.decoder.prenet(
                  output_sequence, speaker_embeddings
              )
  
              # Run the decoder layers on the last element of the prenet output.
              decoder_out = model.speecht5.decoder.wrapped_decoder(
                  hidden_states=decoder_hidden_states[:, -1:],
                  attention_mask=None,
                  encoder_hidden_states=encoder_hidden_states,
                  encoder_attention_mask=encoder_attention_mask,
                  past_key_values=past_key_values,
                  use_cache=use_cache,
                  output_attentions=False,
                  return_dict=True,
              )
  
              last_decoder_output = decoder_out.last_hidden_state[0, -1]
              past_key_values = decoder_out.past_key_values
  
              # Predict the new mel spectrum for this step in the sequence.
              spectrum = model.speech_decoder_postnet.feat_out(
                  last_decoder_output
              )
              spectrum = spectrum.view(
                  model.config.reduction_factor,
                  model.config.num_mel_bins,
              )
  
              # NOTE: extending the spectrogram should is to be handled outside of the ONNX.
              # spectrogram.append(spectrum)
  
              # Extend the output sequence with the new mel spectrum.
              output_sequence = torch.cat(
                  (
                      output_sequence,
                      spectrum[-1].view(
                          1, 1, model.config.num_mel_bins
                      ),
                  ),
                  dim=1,
              )
  
              # Predict the probability that this is the stop token.
              prob = torch.sigmoid(
                  model.speech_decoder_postnet.prob_out(
                      last_decoder_output
                  )
              )
  
              result = {
                  "output_sequence_out": output_sequence,
                  "spectrum": spectrum,
                  "prob": prob,
                  "past_key_values": past_key_values,
              }
          elif self.real_config.is_postnet_and_vocoder:
              # NOTE: the following concatenation is expected to be handled outside of the ONNX:
              # spectrogram = torch.cat(spectrogram, dim=0).unsqueeze(0)
              spectrogram = spectrogram.unsqueeze(0)
              spectrogram = model.speech_decoder_postnet.postnet(
                  spectrogram
              )
              spectrogram = spectrogram.squeeze(0)
  
              waveform = model.vocoder(spectrogram)
  
              result = {"waveform": waveform}
          else:
              raise ValueError("Should not happen")
  
          # Filter out cross attention past key values output from the decoder using KV cache, as they are constants.
          filterd_outputs = {}
          for name, value in result.items():
              if name != "past_key_values":
                  filterd_outputs[name] = value
              else:
                  if self.real_config._behavior == "decoder" and (
                      self.real_config.is_merged
                      or not self.real_config.use_past_in_inputs
                  ):
                      filterd_outputs[name] = value
                  elif (
                      self.real_config._behavior == "decoder"
                      and self.real_config.use_past_in_inputs
                  ):
                      # The filtering happens here. The decoder with use_past_in_inputs=True corresponds to the autoregressive one.
                      filterd_outputs[name] = tuple(
                          [v[:2] for v in value]
                      )
  
          return filterd_outputs
  
      self.patched_forward = patched_forward
  
imports: import dataclasses
  import functools
  import inspect
  import math
  import sys
  import types
  from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
  import transformers
  from packaging import version
  from transformers.models.speecht5.modeling_speecht5 import SpeechT5EncoderWithSpeechPrenet
  from transformers.utils import is_torch_available
  from ...configuration_utils import _transformers_version
  from ...utils import logging
label_desc: Use image node_id 3 to create a new SpeechT5ModelPatcher object from inherited base classes: ModelPatcher with example: obj = SpeechT5ModelPatcher(config, model, model_kwargs)