code: def _sk_rse(target, preds, squared):
      mean = np.mean(target, axis=0, keepdims=True)
      error = target - preds
      sum_squared_error = np.sum(error * error, axis=0)
      deviation = target - mean
      sum_squared_deviation = np.sum(deviation * deviation, axis=0)
      rse = sum_squared_error / np.maximum(
          sum_squared_deviation, 1.17e-06
      )
      if not squared:
          rse = np.sqrt(rse)
      return np.mean(rse)
  
imports: from functools import partial
  import numpy
  import pytest
  import torch
  from torchmetrics.functional import relative_squared_error
  from torchmetrics.regression import RelativeSquaredError
  from torchmetrics.utilities.imports import _TORCH_GREATER_EQUAL_2_1
  from unittests import BATCH_SIZE, NUM_BATCHES, _Input
  from unittests.helpers import seed_all
  from unittests.helpers.testers import MetricTester
label_desc: Use image node_id 1 for calling a global function with example usage: _sk_rse(target, preds, squared) and returns: np