code: def log_loss(p: NDArray[Float], y: NDArray[Float]) -> NDArray[Float]:
      """
      Computes the log loss of probability values p and observed binary variable y.
  
      Args:
          p : probability values, assumed to have shape n obervations x m models
          y : observed binary variable, assumed to have shape n observations x 1
  
      Returns:
          Array of [n * m]
      """
      eps = 1e-15
      return (
          -(y * np.log(p + eps) + (1 - y) * np.log(1 - p + eps)).sum(
              axis=0
          )
          / y.shape[0]
      )
  
imports: import itertools
  import math
  import re
  import warnings
  from typing import Any, Dict, Iterable, List, Tuple
  import numpy
  import pandas
  import pyspark.sql.functions
  import scipy.optimize
  from nptyping import Float, Int, NDArray
  from pyspark.sql import DataFrame, Row
  from pyspark.sql.window import Window
  from typeguard import typechecked
label_desc: Use image node_id 14 for calling a global function with example usage: log_loss(p, y) and returns: unknown