code: def forward(
      self,
      input_ids,
      attention_mask,
      inputs_embeds=None,
      head_mask=None,
      output_attentions=None,
      output_hidden_states=None,
      return_dict=None,
  ):
      encoder_hidden_state = torch.from_numpy(
          self.encoder.run(
              None,
              {
                  "input_ids": input_ids.cpu().numpy(),
                  "attention_mask": attention_mask.cpu().numpy(),
              },
          )[0]
      )
  
      return BaseModelOutput(encoder_hidden_state)
  
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 T5Encoder obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, inputs_embeds, head_mask, output_attentions, output_hidden_states, return_dict) and returns: BaseModelOutput