code: def pad_contrast_matrix(contrast_def, design_matrix):
      """Pad contrasts with zeros.
  
      Parameters
      ----------
      contrast_def : :class:`numpy.ndarray`
          Contrast to be padded
  
      design_matrix : :class:`pandas.DataFrame`
          Design matrix to use.
  
      Returns
      -------
      ax : :class:`numpy.ndarray`
          Padded contrast
  
      """
      design_column_names = design_matrix.columns.tolist()
      if isinstance(contrast_def, str):
          contrast_def = expression_to_contrast_vector(
              contrast_def, design_column_names
          )
      nb_columns_design_matrix = len(design_column_names)
      nb_columns_contrast_def = (
          contrast_def.shape[0]
          if contrast_def.ndim == 1
          else contrast_def.shape[1]
      )
      horizontal_padding = (
          nb_columns_design_matrix - nb_columns_contrast_def
      )
      if horizontal_padding == 0:
          return contrast_def
      warnings.warn(
          (
              f"Contrasts will be padded with {horizontal_padding} "
              "column(s) of zeros."
          ),
          category=UserWarning,
          stacklevel=3,
      )
      contrast_def = np.pad(
          contrast_def,
          ((0, 0), (0, horizontal_padding)),
          "constant",
          constant_values=(0, 0),
      )
      return contrast_def
  
imports: import warnings
  import matplotlib.patches
  import matplotlib.pyplot
  import numpy
  import pandas
  from mpl_toolkits.axes_grid1 import make_axes_locatable
  from scipy.cluster.hierarchy import leaves_list, linkage, optimal_leaf_ordering
  from nilearn.glm.first_level.experimental_paradigm import check_events
  from .._utils import fill_doc
label_desc: Use image node_id 13 for calling a global function with example usage: pad_contrast_matrix(contrast_def, design_matrix) and returns: contrast_def, contrast_def