code: def get_baseline(
      model: LudwigModel, sample_encoded: List[Variable]
  ) -> List[torch.Tensor]:
      # TODO(travis): pre-compute this during training from the full training dataset.
      input_features: LudwigFeatureDict = model.model.input_features
  
      baselines = []
      for sample_input, (name, feature) in zip(
          sample_encoded, input_features.items()
      ):
          metadata = model.training_set_metadata[name]
          if feature.type() == TEXT:
              PAD_IND = metadata.get(
                  "pad_idx", metadata.get("word_pad_idx")
              )
              token_reference = TokenReferenceBase(
                  reference_token_idx=PAD_IND
              )
              baseline = token_reference.generate_reference(
                  sequence_length=sample_input.shape[1], device=DEVICE
              )
          elif feature.type() == CATEGORY:
              most_popular_token = max(
                  metadata["str2freq"], key=metadata["str2freq"].get
              )
              most_popular_tok_idx = metadata["str2idx"].get(
                  most_popular_token
              )
  
              # If an unknown is defined, use that as the baseline index, else use the most popular token
              baseline_tok_idx = metadata["str2idx"].get(
                  UNKNOWN_SYMBOL, most_popular_tok_idx
              )
              baseline = torch.tensor(baseline_tok_idx, device=DEVICE)
          elif feature.type() == IMAGE:
              baseline = torch.zeros_like(
                  sample_input[0], device=DEVICE
              )
          else:
              # For a robust baseline, we take the mean of all samples from the training data.
              baseline = torch.mean(sample_input.float(), dim=0)
          baselines.append(baseline.unsqueeze(0))
  
      return baselines
  
imports: import copy
  import gc
  import logging
  from collections import defaultdict
  from dataclasses import dataclass
  from typing import Dict, List, Optional, Tuple
  import numpy
  import numpy.typing
  import pandas
  import torch
  from captum.attr import LayerIntegratedGradients, TokenReferenceBase
  from captum.attr._utils.input_layer_wrapper import InputIdentity
  from torch.autograd import Variable
  from tqdm import tqdm
  from ludwig.api import LudwigModel
  from ludwig.api_annotations import PublicAPI
  from ludwig.constants import BINARY, CATEGORY, DATE, IMAGE, INPUT_FEATURES, MINIMUM_BATCH_SIZE, NAME, NUMBER, PREPROCESSING, SEQUENCE, SET, TEXT, UNKNOWN_SYMBOL
  from ludwig.data.preprocessing import preprocess_for_prediction
  from ludwig.explain.explainer import Explainer
  from ludwig.explain.explanation import ExplanationsResult
  from ludwig.explain.util import get_pred_col, replace_layer_with_copy
  from ludwig.features.feature_utils import LudwigFeatureDict
  from ludwig.models.ecd import ECD
  from ludwig.utils.torch_utils import DEVICE
label_desc: Use image node_id 3 for calling a global function with example usage: get_baseline(model, sample_encoded) and returns: baselines