code: def data_check(data, target):
      """Checks data type
  
      Parameters
      ----------
      data : pd.DataFrame or np.array
          Field to specify the time series data that will be used.
  
      target : int or str
          Target column
  
      Returns
      ----------
      transformed_data : np.array
          Raw data array for use in the model
  
      data_name : str
          Name of the data
  
      is_pandas : Boolean
          True if pandas data, else numpy
  
      data_index : np.array
          The time indices for the data
      """
  
      # Check pandas or numpy
      if isinstance(data, pd.DataFrame) or isinstance(
          data, pd.core.frame.DataFrame
      ):
          data_index = data.index
          if target is None:
              transformed_data = data.ix[:, 0].values
              data_name = str(data.columns.values[0])
          else:
              transformed_data = data[target].values
              data_name = str(target)
          is_pandas = True
  
      elif isinstance(data, np.ndarray):
          data_name = "Series"
          is_pandas = False
          if any(isinstance(i, np.ndarray) for i in data):
              if target is None:
                  transformed_data = data[0]
                  data_index = list(range(len(data[0])))
              else:
                  transformed_data = data[target]
                  data_index = list(range(len(data[target])))
          else:
              transformed_data = data
              data_index = list(range(len(data)))
      else:
          raise Exception(
              "The data input is not pandas or numpy compatible!"
          )
  
      return transformed_data, data_name, is_pandas, data_index
  
label_desc: Use image node_id 1 for calling a global function with example usage: data_check(data, target) and returns: transformed_data,  data_name,  is_pandas,  data_index