code: def forward(
      self,
      inputs: torch.Tensor,
      head_mask: Optional[torch.Tensor] = None,
  ) -> EncoderOutputDict:
      output = self.transformer.module(
          inputs,
          head_mask=head_mask,
          output_attentions=self.output_attentions,
      )
      return_dict: EncoderOutputDict = {
          ENCODER_OUTPUT: output.pooler_output
      }
      if self.output_attentions:
          return_dict["attentions"] = output.attentions
      return return_dict
  
imports: import logging
  from typing import Any, Dict, List, Optional, Tuple, Type, Union
  import torch
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.constants import ENCODER_OUTPUT, IMAGE
  from ludwig.encoders.base import Encoder
  from ludwig.encoders.registry import register_encoder
  from ludwig.encoders.types import EncoderOutputDict
  from ludwig.modules.convolutional_modules import Conv2DStack, ResNet
  from ludwig.modules.fully_connected_modules import FCStack
  from ludwig.modules.mlp_mixer_modules import MLPMixer
  from ludwig.schema.encoders.image.base import ImageEncoderConfig, MLPMixerConfig, ResNetConfig, Stacked2DCNNConfig, ViTConfig
  from ludwig.utils.torch_utils import FreezeModule
label_desc: Use image node_id 2 for calling the ViTEncoder obj's underlying member method code with example usage: obj.forward(inputs, head_mask) and returns: return_dict