code: def moment_exponential_add(params, exog, exp=True):
      if not np.isfinite(params).all():
          print("invalid params", params)
  
      # moment condition without instrument
      if exp:
          predicted = np.exp(np.dot(exog, params))
          # if not np.isfinite(predicted).all():
          # print "invalid predicted", predicted
          # raise RuntimeError('invalid predicted')
          predicted = np.clip(predicted, 0, 1e100)  # try to avoid inf
      else:
          predicted = np.dot(exog, params)
  
      return predicted
  
imports: from statsmodels.compat.python import lmap
  import numpy
  import pandas
  from scipy import stats
  import pytest
  from statsmodels.regression.linear_model import OLS
  from statsmodels.sandbox.regression import gmm
  from numpy.testing import assert_allclose, assert_equal
label_desc: Use image node_id 2 for calling a global function with example usage: moment_exponential_add(params, exog, exp) and returns: predicted