code: def forward(
      self, input_ids, attention_mask, encoder_output, past_key_values
  ):
      decoder_inputs = {
          "input_ids": input_ids.cpu().numpy(),
          "encoder_attention_mask": attention_mask.cpu().numpy(),
          "encoder_hidden_states": encoder_output.cpu().numpy(),
      }
  
      flat_past_key_values = functools.reduce(
          operator.iconcat, past_key_values, []
      )
  
      past_key_values = {
          f"pkv_{i}": pkv.cpu().numpy()
          for i, pkv in enumerate(flat_past_key_values)
      }
  
      decoder_outputs = self.decoder.run(
          None, {**decoder_inputs, **past_key_values}
      )
      # converts each value of the list to tensor from numpy
      list_pkv = tuple(torch.from_numpy(x) for x in decoder_outputs[1:])
  
      # creates a tuple of tuples of shape 6x4 from the above tuple
      out_past_key_values = tuple(
          list_pkv[i : i + 4] for i in range(0, len(list_pkv), 4)
      )
  
      return torch.from_numpy(decoder_outputs[0]), out_past_key_values
  
imports: from .huggingface_utils import get_auth_token
  from .ort_settings import get_onnx_runtime_sessions
  from .onnx_exporter import generate_onnx_representation, quantize, get_model_paths, saved_models_path
  from pathlib import Path
  from transformers import AutoConfig, MT5Config, T5ForConditionalGeneration
  from transformers.modeling_outputs import Seq2SeqLMOutput, BaseModelOutput
  import torch
  import functools
  import operator
label_desc: Use image node_id 2 for calling the T5Decoder obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, encoder_output, past_key_values) and returns: torch,  out_past_key_values