code: def assert_multi_label_shape(y):
      if not isinstance(y, np.ndarray):
          raise DeepchecksValueError(
              f"Expected y to be numpy array instead got: {type(y)}"
          )
      if y.ndim != 2:
          raise DeepchecksValueError(
              f"Expected y to be numpy array with 2 dimensions instead got {y.ndim} dimensions."
          )
      assert_binary_values(y)
      # Since the metrics are not yet supporting real multi-label, make sure there isn't any row with sum larger than 1
      if y.sum(axis=1).max() > 1:
          raise DeepchecksValueError(
              "Multi label scorers are not supported yet, the sum of a row in multi-label format "
              "must not be larger than 1"
          )
  
file_path: deepchecks/deepchecks/tabular/metric_utils/additional_classification_metrics.py
imports: from typing import Union
  import numpy
  from sklearn.metrics import confusion_matrix, roc_auc_score
  from deepchecks.core.errors import DeepchecksValueError
  from deepchecks.utils.metrics import averaging_mechanism
label_desc: Use image node_id 2 for calling a global function with example usage: assert_multi_label_shape(y) without return types