code: def _raise_error_if_not_drawing_classifier_input_sframe(
      dataset, feature, target):
      
      from turicreate.toolkits._internal_utils import _raise_error_if_not_sframe
      _raise_error_if_not_sframe(dataset)
      if feature not in dataset.column_names():
          raise _ToolkitError("Feature column '%s' does not exist" % feature)
      if target not in dataset.column_names():
          raise _ToolkitError("Target column '%s' does not exist" % target)
      if (dataset[feature].dtype != _tc.Image and dataset[feature].dtype != list):
          raise _ToolkitError("Feature column must contain images" 
              + " or stroke-based drawings encoded as lists of strokes" 
              + " where each stroke is a list of points and" 
              + " each point is stored as a dictionary")
      if dataset[target].dtype != int and dataset[target].dtype != str:
          raise _ToolkitError("Target column contains " + str(dataset[target].dtype)
              + " but it must contain strings or integers to represent" 
              + " labels for drawings.")
      if len(dataset) == 0:
          raise _ToolkitError("Input Dataset is empty!")
docs: Performs some sanity checks on the SFrame provided as input to 
      `turicreate.drawing_classifier.create` and raises a ToolkitError
      if something in the dataset is missing or wrong.