code: def _unflatten_dict_by_feature_name(
      flattened_dict: Dict[str, Any]
  ) -> Dict[str, Dict[str, Any]]:
      """Convert a flattened dictionary of objects to a nested dictionary of outputs per feature name."""
      outputs: Dict[str, Dict[str, Any]] = {}
      for concat_key, tensor_values in flattened_dict.items():
          feature_name = get_feature_name_from_concat_name(concat_key)
          tensor_name = get_tensor_name_from_concat_name(concat_key)
          feature_outputs: Dict[str, Any] = {}
          if feature_name not in outputs:
              outputs[feature_name] = feature_outputs
          else:
              feature_outputs = outputs[feature_name]
          feature_outputs[tensor_name] = tensor_values
      return outputs
  
imports: import logging
  import os
  from typing import Any, Dict, Optional, TYPE_CHECKING, Union
  import pandas
  import torch
  from torch import nn
  from ludwig.constants import NAME, POSTPROCESSOR, PREDICTOR, PREPROCESSOR, TYPE
  from ludwig.data.postprocessing import convert_dict_to_df
  from ludwig.data.preprocessing import load_metadata
  from ludwig.features.feature_registries import get_input_type_registry
  from ludwig.features.feature_utils import get_module_dict_key_from_name, get_name_from_module_dict_key
  from ludwig.globals import MODEL_HYPERPARAMETERS_FILE_NAME, TRAIN_SET_METADATA_FILE_NAME
  from ludwig.types import ModelConfigDict, TrainingSetMetadataDict
  from ludwig.utils import output_feature_utils
  from ludwig.utils.data_utils import load_json, save_json
  from ludwig.utils.inference_utils import get_filename_from_stage, to_inference_module_input_from_dataframe
  from ludwig.utils.misc_utils import get_from_registry
  from ludwig.utils.output_feature_utils import get_feature_name_from_concat_name, get_tensor_name_from_concat_name
  from ludwig.utils.torch_utils import DEVICE
  from ludwig.utils.types import TorchDevice, TorchscriptPreprocessingInput
label_desc: Use image node_id 4 for calling a global function with example usage: _unflatten_dict_by_feature_name(flattened_dict) and returns: outputs