def _full_hessian(self, grads, predictions):
    'Prepares hessians for full-hessian multiclass strategy.'
    gradients_list = array_ops.unstack(grads, num=self._logits_dimension, axis=1)
    hessian_rows = []
    for row in range(self._logits_dimension):
        hessian_row = gradients_impl.gradients(gradients_list[row], predictions, name=('Hessian_%d' % row), colocate_gradients_with_ops=False, gate_gradients=0, aggregation_method=None)
        hessian_row = array_ops.squeeze(array_ops.unstack(hessian_row), [0])
        hessian_rows.append(hessian_row)
    return hessian_rows
Prepares hessians for full-hessian multiclass strategy.
def _full_hessian(self, grads, predictions):
    
    gradients_list = array_ops.unstack(grads, num=self._logits_dimension, axis=1)
    hessian_rows = []
    for row in range(self._logits_dimension):
        hessian_row = gradients_impl.gradients(gradients_list[row], predictions, name=('Hessian_%d' % row), colocate_gradients_with_ops=False, gate_gradients=0, aggregation_method=None)
        hessian_row = array_ops.squeeze(array_ops.unstack(hessian_row), [0])
        hessian_rows.append(hessian_row)
    return hessian_rows