code: def get_numeric_gradient(predict, n, target):
      gradient = numpy.zeros(n)
      for i in range(n):
          out1 = predict(i, 1e-4)
          out2 = predict(i, -1e-4)
  
          err1 = _get_loss(out1, target)
          err2 = _get_loss(out2, target)
          gradient[i] = (err1 - err2) / (2 * 1e-4)
          print("NGrad", i, err1, err2)
      return gradient
  
imports: from functools import partial
  import numpy
  import pytest
  from numpy.testing import assert_allclose
  from thinc.api import Linear, NumpyOps, Relu, chain
label_desc: Use image node_id 19 for calling a global function with example usage: get_numeric_gradient(predict, n, target) and returns: gradient