code: def _check_targets_input(targets, data):
      """Checks ISFC targets input array
  
      For ISFC analysis, targets input array should either be a list
      of n_TRs by n_targets arrays (where each array corresponds to
      a subject), or an n_TRs by n_targets by n_subjects ndarray. This
      function also checks the shape of the targets array against the
      input data array.
  
      Parameters
      ----------
      data : list or ndarray (n_TRs x n_voxels x n_subjects)
          fMRI data for which to compute ISFC
  
      targets : list or ndarray (n_TRs x n_voxels x n_subjects)
          fMRI data to use as targets for ISFC
  
      Returns
      -------
      targets : ndarray (n_TRs x n_voxels x n_subjects)
          ISFC targets with standadized structure
  
      n_TRs : int
          Number of time points (TRs) for targets array
  
      n_voxels : int
          Number of voxels (or ROIs) for targets array
  
      n_subjects : int
          Number of subjects for targets array
  
      symmetric : bool
          Indicator for symmetric vs. asymmetric
      """
  
      if isinstance(targets, np.ndarray) or isinstance(targets, list):
          (
              targets,
              n_TRs,
              n_voxels,
              n_subjects,
          ) = _check_timeseries_input(targets)
          if data.shape[0] != n_TRs:
              raise ValueError(
                  "Targets array must have same number of "
                  "TRs as input data"
              )
          if data.shape[2] != n_subjects:
              raise ValueError(
                  "Targets array must have same number of "
                  "subjects as input data"
              )
          symmetric = False
      else:
          targets = data
          n_TRs, n_voxels, n_subjects = data.shape
          symmetric = True
  
      return targets, n_TRs, n_voxels, n_subjects, symmetric
  
imports: import numpy
  import logging
  from scipy.spatial.distance import squareform
  from itertools import combinations, permutations, product
  from brainiak.fcma.util import compute_correlation
  from brainiak.utils.utils import array_correlation, phase_randomize, p_from_null, _check_timeseries_input
label_desc: Use image node_id 4 for calling a global function with example usage: _check_targets_input(targets, data) and returns: targets,  n_TRs,  n_voxels,  n_subjects,  symmetric