old_contents: """Autoregressive Integrated Moving Average Imputation"""
  import numpy as np
  from impyute.utils import find_null
  from impyute.utils import checks
  
  
  def arima(data, p, d, q):
      """Autoregressive Integrated Moving Average Imputation
  
      PARAMETERS
      ----------
      data: numpy.ndarray
          The matrix with missing values that you want to impute
      p: int
          Number of autoregressive terms
      d: int
          Number of nonseasonal differences needed for stationarity
      q: int
          Number of lagged forecast errors in the prediction equation
      RETURNS
      -------
      numpy.ndarray
      """
      # Verify inputs
      if not checks(data):
          raise Exception("Checks failed")
      try:
          p = int(p)
          d = int(d)
          q = int(q)
          data = isinstance(data, np.array)
      except:
          raise Exception
      # Arima
      null_xy = find_null(data)
      for x, y in null_xy:
          print(x, y)
      return data
  
new_contents: """Autoregressive Integrated Moving Average Imputation"""
  import numpy as np
  from impyute.utils import find_null
  from impyute.utils import checks
  
  
  def arima(data, p, d, q):
      """Autoregressive Integrated Moving Average Imputation
  
      PARAMETERS
      ----------
      data: numpy.ndarray
          The matrix with missing values that you want to impute
      p: int
          Number of autoregressive terms
      d: int
          Number of nonseasonal differences needed for stationarity
      q: int
          Number of lagged forecast errors in the prediction equation
      RETURNS
      -------
      numpy.ndarray
      """
      # Verify inputs
      if not checks(data):
          raise Exception("Checks failed")
      try:
          p = int(p)
          d = int(d)
          q = int(q)
          data = isinstance(data, np.ndarray)
      except:
          raise Exception
      # Arima
      null_xy = find_null(data)
      for x, y in null_xy:
          print(x, y)
      return data
  
subject: Fix isinstance()