code: def forward(self, v: TorchscriptPreprocessingInput) -> torch.Tensor:
      if torch.jit.isinstance(v, List[torch.Tensor]):
          v = torch.stack(v)
  
      if not torch.jit.isinstance(v, torch.Tensor):
          raise ValueError(f"Unsupported input: {v}")
  
      v = torch.nan_to_num(v, nan=self.computed_fill_value)
      v = v.long()
  
      outputs: List[torch.Tensor] = []
      for v_i in v:
          components = h3_to_components(v_i)
          header: List[int] = [
              components.mode,
              components.edge,
              components.resolution,
              components.base_cell,
          ]
          cells_padding: List[int] = [self.h3_padding_value] * (
              self.max_h3_resolution - len(components.cells)
          )
          output = torch.tensor(
              header + components.cells + cells_padding,
              dtype=torch.uint8,
              device=v.device,
          )
          outputs.append(output)
  
      return torch.stack(outputs)
  
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 _H3Preprocessing obj's underlying member method code with example usage: obj.forward(v) and returns: torch