ensemble_hic/error_models.py
def _evaluate_gradient(self, **variables):
        """
        In theory, this evaluates the gradient of the negative log-probability,
        but I usually hardcode the multiplication of this with the forward
        model Jacobian in Cython (see :mod:`.likelihoods_c`)
        """
        pass

        In theory, this evaluates the gradient of the negative log-probability,
        but I usually hardcode the multiplication of this with the forward
        model Jacobian in Cython (see :mod:`.likelihoods_c`)
        
In theory, this evaluates the gradient of the negative log-probability,
but I usually hardcode the multiplication of this with the forward
model Jacobian in Cython
In theory this evaluates the gradient of the negative log - probability but I usually hardcode the multiplication of this with the forward model Jacobian in Cython
def _evaluate_gradient(self, **variables):
        pass
def _evaluate_gradient ( self , ** variables ) : pass
In theory, this evaluates the gradient of the negative log-probability,
but I usually hardcode the multiplication of this with the forward
model Jacobian in Cython (see :mod:`.likelihoods_c`)
In theory this evaluates the gradient of the negative log - probability but I usually hardcode the multiplication of this with the forward model Jacobian in Cython ( see : mod : ` . likelihoods_c ` )
"""
        In theory, this evaluates the gradient of the negative log-probability,
        but I usually hardcode the multiplication of this with the forward
        model Jacobian in Cython (see :mod:`.likelihoods_c`)
        """
self