code: def forward(self, inputs):
      assert isinstance(inputs, torch.Tensor)
      assert inputs.dtype in [torch.uint8, torch.int64]
      assert len(inputs.shape) == 2
  
      inputs_encoded = self.encoder_obj(inputs)
  
      return inputs_encoded
  
imports: import logging
  from typing import List
  import numpy
  import torch
  from ludwig.constants import COLUMN, H3, PROC_COLUMN
  from ludwig.features.base_feature import BaseFeatureMixin, InputFeature
  from ludwig.schema.features.h3_feature import H3InputFeatureConfig
  from ludwig.types import FeatureMetadataDict, ModelConfigDict, PreprocessingConfigDict, TrainingSetMetadataDict
  from ludwig.utils.h3_util import h3_to_components
  from ludwig.utils.types import TorchscriptPreprocessingInput
label_desc: Use image node_id 2 for calling the H3InputFeature obj's underlying member method code with example usage: obj.forward(inputs) and returns: inputs_encoded