code: def _weighted_sum(sample_score, sample_weight, normalize=False):
      if normalize:
          return mt.average(sample_score, weights=sample_weight)
      elif sample_weight is not None:
          return mt.dot(sample_score, sample_weight)
      else:
          return sample_score.sum()
  
imports: import warnings
  import numpy
  from sklearn.exceptions import UndefinedMetricWarning
  from ...None import execute, fetch
  from ...None import opcodes
  from ...None import tensor
  from ...core import ENTITY_TYPE, recursive_tile
  from ...core.context import get_context
  from ...serialization.serializables import AnyField, BoolField, KeyField
  from ...tensor.core import TensorOrder
  from ..operands import LearnOperand, LearnOperandMixin, OutputType
  from ..preprocessing import LabelBinarizer, LabelEncoder
  from ..utils import check_array, check_consistent_length, column_or_1d
  from ..utils.multiclass import unique_labels
  from ..utils.sparsefuncs import count_nonzero
  from ._check_targets import _check_targets
label_desc: Use image node_id 1 for calling a global function with example usage: _weighted_sum(sample_score, sample_weight, normalize) and returns: mt, mt, sample_score