code: def encode(
      self,
      inputs: Union[
          Dict[str, torch.Tensor],
          Dict[str, np.ndarray],
          Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor]],
      ],
  ):
      # Convert inputs to tensors.
      for input_feature_name, input_values in inputs.items():
          if not isinstance(input_values, torch.Tensor):
              inputs[input_feature_name] = torch.from_numpy(
                  input_values
              )
          else:
              inputs[input_feature_name] = input_values
  
      encoder_outputs = {}
      for input_feature_name, input_values in inputs.items():
          encoder = self.input_features.get(input_feature_name)
          encoder_output = encoder(input_values)
          encoder_outputs[input_feature_name] = encoder_output
  
      return encoder_outputs
  
imports: import logging
  import os
  from typing import Dict, Tuple, Union
  import numpy
  import torch
  from ludwig.accounting.used_tokens import get_used_tokens_for_ecd
  from ludwig.combiners.combiners import create_combiner
  from ludwig.constants import MODEL_ECD, MODEL_LLM, USED_TOKENS
  from ludwig.globals import MODEL_WEIGHTS_FILE_NAME
  from ludwig.models.base import BaseModel
  from ludwig.schema.model_types.ecd import ECDModelConfig
  from ludwig.utils import output_feature_utils
  from ludwig.utils.augmentation_utils import AugmentationPipelines
  from ludwig.utils.data_utils import clear_data_cache
  from ludwig.utils.fs_utils import open_file
  from ludwig.utils.state_dict_backward_compatibility import update_state_dict
  from ludwig.utils.torch_utils import get_torch_device
label_desc: Use image node_id 4 for calling the ECD obj's underlying member method code with example usage: obj.encode(inputs) and returns: encoder_outputs