code: def _standardize_input(y_true, y_pred, multioutput):
      """
      This function check the validation of the input
      input should be one of list/tuple/ndarray with same shape and not be None
      input dtype will be cast to np.float64
      input will be changed to corresponding 2-dim ndarray
      """
      if y_true is None or y_pred is None:
          raise ValueError("The input is None.")
      if not isinstance(
          y_true, (list, tuple, np.ndarray, pd.DataFrame)
      ):
          raise ValueError(
              "Expected array-like input."
              "Only list/tuple/ndarray/pd.DataFrame are supported"
          )
      if isinstance(y_true, (list, tuple)):
          y_true = np.array(y_true)
      if isinstance(y_pred, (list, tuple)):
          y_pred = np.array(y_pred)
      if isinstance(y_true, pd.DataFrame):
          y_true = y_true.to_numpy()
      if isinstance(y_pred, pd.DataFrame):
          y_pred = y_pred.to_numpy()
  
      y_true = y_true.astype(np.float64)
      y_pred = y_pred.astype(np.float64)
  
      original_shape = y_true.shape[1:]
  
      if y_true.ndim == 1:
          y_true = y_true.reshape((-1, 1))
      else:
          y_true = y_true.reshape((y_true.shape[0], -1))
  
      if y_pred.ndim == 1:
          y_pred = y_pred.reshape((-1, 1))
      else:
          y_pred = y_pred.reshape((y_pred.shape[0], -1))
  
      if y_true.shape[0] != y_pred.shape[0]:
          raise ValueError(
              "y_true and y_pred have different number of samples "
              "({0}!={1})".format(y_true.shape[0], y_pred.shape[0])
          )
      if y_true.shape[1] != y_pred.shape[1]:
          raise ValueError(
              "y_true and y_pred have different number of output "
              "({0}!={1})".format(y_true.shape[1], y_pred.shape[1])
          )
      allowed_multioutput_str = (
          "raw_values",
          "uniform_average",
          "variance_weighted",
      )
      if isinstance(multioutput, str):
          if multioutput not in allowed_multioutput_str:
              raise ValueError(
                  "Allowed 'multioutput' string values are {}. "
                  "You provided multioutput={!r}".format(
                      allowed_multioutput_str, multioutput
                  )
              )
  
      return y_true, y_pred, original_shape
  
imports: from sklearn.metrics import mean_squared_error
  from sklearn.metrics import r2_score
  from sklearn.metrics import mean_absolute_error
  from sklearn.metrics import mean_squared_log_error
  import numpy
  import pandas
label_desc: Use image node_id 1 for calling a global function with example usage: _standardize_input(y_true, y_pred, multioutput) and returns: y_true,  y_pred,  original_shape