class_docstr: The Sarcos dataset.
  
  Details:
      The data relates to an inverse dynamics problem for a seven
      degrees-of-freedom SARCOS anthropomorphic robot arm. The
      task is to map from a 21-dimensional input space (7 joint
      positions, 7 joint velocities, 7 joint accelerations) to the
      corresponding 7 joint torques. There are 44,484 training
      examples and 4,449 test examples. The first 21 columns are
      the input variables, and the 22nd column is used as the target
      variable.
  
  Dataset source:
      Locally Weighted Projection RegressionL: An O(n) Algorithm for
      Incremental Real Time Learning in High Dimensional Space,
      S. Vijayakumar and S. Schaal, Proc ICML 2000.
      http://www.gaussianprocess.org/gpml/data/
code: def transform_dataframe(
      self, dataframe: pd.DataFrame
  ) -> pd.DataFrame:
      processed_df = super().transform_dataframe(dataframe)
      columns = []
      columns += [f"position_{i}" for i in range(1, 8)]
      columns += [f"velocity_{i}" for i in range(1, 8)]
      columns += [f"acceleration_{i}" for i in range(1, 8)]
      columns += [f"torque_{i}" for i in range(1, 8)]
      columns += ["split"]
  
      processed_df.columns = columns
      return processed_df
  
file_path: ludwig/ludwig/datasets/loaders/sarcos.py
imports: import os
  import pandas
  from scipy.io import loadmat
  from ludwig.datasets.loaders.dataset_loader import DatasetLoader
  from ludwig.utils.fs_utils import open_file
label_desc: Use image node_id 2 for calling the SarcosLoader obj's underlying member method code with example usage: obj.transform_dataframe(dataframe) and returns: processed_df