code: def __init__(
      self,
      df,
      cat_columns,
      cont_columns,
      label_columns,
      batch_size=32,
      shuffle=False,
  ):
      # error checks
      if not isinstance(df, pd.DataFrame):
          raise ValueError("df must be pandas Dataframe")
      all_columns = cat_columns + cont_columns + label_columns
      missing_columns = []
      for col in df.columns.values:
          if col not in all_columns:
              missing_columns.append(col)
      if len(missing_columns) > 0:
          raise ValueError(
              "df is missing these columns: %s" % (missing_columns)
          )
  
      # set variables
      super().__init__(batch_size=batch_size)
      self.indices = np.arange(df.shape[0])
      self.df = df
      self.cat_columns = cat_columns
      self.cont_columns = cont_columns
      self.label_columns = label_columns
      self.shuffle = shuffle
  
file_path: ktrain/ktrain/tabular/dataset.py
imports: from ..dataset import SequenceDataset
  from ..imports import *
label_desc: Use image node_id 1 to create a new TabularDataset object from inherited base classes: SequenceDataset with example: obj = TabularDataset(df, cat_columns, cont_columns, label_columns, batch_size, shuffle)