code: def patched_speecht5_prenet_forward(
      self,
      input_values: torch.Tensor,
      speaker_embeddings: Optional[torch.Tensor] = None,
  ):
      # Dropout is always applied, even when evaluating. See §2.2 in https://arxiv.org/abs/1712.05884.
  
      inputs_embeds = input_values
      for layer in self.layers:
          inputs_embeds = torch.nn.functional.relu(layer(inputs_embeds))
  
          # NOTE: we patch the prenet to avoid using torch.nn.functional.dropout, that is exported as a `Dropout` node in the ONNX
          # that is ignored during inference by some runtimes as ONNX Runtime.
          # Reference: https://github.com/microsoft/onnxruntime/issues/9333 & https://github.com/microsoft/onnxruntime/issues/5549
          mask = (
              torch.rand(
                  inputs_embeds.shape, device=inputs_embeds.device
              )
              > self.config.speech_decoder_prenet_dropout
          )
          inputs_embeds = (
              inputs_embeds
              * mask
              / (1 - self.config.speech_decoder_prenet_dropout)
          )
  
          # inputs_embeds = nn.functional.dropout(
          #     inputs_embeds, self.config.speech_decoder_prenet_dropout, training=True
          # )
  
      inputs_embeds = self.final_layer(inputs_embeds)
      inputs_embeds = self.encode_positions(inputs_embeds)
  
      if speaker_embeddings is not None:
          speaker_embeddings = torch.nn.functional.normalize(
              speaker_embeddings
          )
          speaker_embeddings = speaker_embeddings.unsqueeze(1)
          speaker_embeddings = speaker_embeddings.expand(
              -1, inputs_embeds.size(1), -1
          )
          inputs_embeds = torch.cat(
              [inputs_embeds, speaker_embeddings], dim=-1
          )
          inputs_embeds = torch.nn.functional.relu(
              self.speaker_embeds_layer(inputs_embeds)
          )
  
      return inputs_embeds
  
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 7 for calling a global function with example usage: patched_speecht5_prenet_forward(input_values, speaker_embeddings) and returns: inputs_embeds