docstr: Prepare data for inference.
docstr_tok: ["Prepare","data","for","inference","."]
code: def _prepare_inference_data(self, df: DataFrame) -> DataFrame:
      """Prepare data for inference."""
      # TODO Is the target encoding necessary?
      if len(set(self.target) - set(df.columns)) > 0:
          if self.config.task == "classification":
              df.loc[:, self.target] = np.array(
                  [self.label_encoder.classes_[0]] * len(df)
              ).reshape(-1, 1)
          else:
              df.loc[:, self.target] = np.zeros(
                  (len(df), len(self.target))
              )
      df, _ = self.preprocess_data(df, stage="inference")
      return df
  
imports: import re
  import warnings
  from enum import Enum
  from pathlib import Path
  from typing import Dict, Iterable, List, Optional, Tuple, Union
  import joblib
  import numpy
  import pandas
  import pytorch_lightning
  import torch
  from omegaconf import DictConfig
  from pandas import DataFrame, DatetimeTZDtype, to_datetime
  from pandas.tseries import offsets
  from pandas.tseries.frequencies import to_offset
  from sklearn.base import TransformerMixin, copy
  from sklearn.preprocessing import FunctionTransformer, LabelEncoder, PowerTransformer, QuantileTransformer, StandardScaler
  from torch.utils.data import DataLoader, Dataset
  from pytorch_tabular.config import InferredConfig
  from pytorch_tabular.utils import get_logger
  from .categorical_encoders import OrdinalEncoder
label_desc: Use image node_id 37 for calling the TabularDatamodule obj's underlying member method code with example usage: obj._prepare_inference_data(df) and returns: df