Python six.create_bound_method() Examples

def getLogger(name):
    """Create logger with custom exception() method
    """
    def exception(self, msg, *args, **kwargs):
        extra = kwargs.setdefault('extra', {})
        extra['exc_fullstack'] = self.isEnabledFor(logging.DEBUG)
        kwargs['exc_info'] = True
        self.log(logging.ERROR, msg, *args, **kwargs)

    logger = logging.getLogger(name)
    logger.exception = six.create_bound_method(exception, logger)
    return logger 

def test_create_bound_method():
    class X(object):
        pass
    def f(self):
        return self
    x = X()
    b = six.create_bound_method(f, x)
    assert isinstance(b, types.MethodType)
    assert b() is x 

def to_graph(func, recursive=True):
  new_func = tf.autograph.to_graph(
      func,
      recursive=recursive,
      experimental_optional_features=tf.autograph.experimental.Feature.ALL)
  # TODO(b/127686409): Remove this.
  if inspect.ismethod(func):
    return six.create_bound_method(new_func, func.__self__)
  return new_func 

def test_create_bound_method():
    class X(object):
        pass
    def f(self):
        return self
    x = X()
    b = six.create_bound_method(f, x)
    assert isinstance(b, types.MethodType)
    assert b() is x 

def test_create_bound_method():
    class X(object):
        pass
    def f(self):
        return self
    x = X()
    b = six.create_bound_method(f, x)
    assert isinstance(b, types.MethodType)
    assert b() is x 

def test_create_bound_method():
    class X(object):
        pass
    def f(self):
        return self
    x = X()
    b = six.create_bound_method(f, x)
    assert isinstance(b, types.MethodType)
    assert b() is x 

def __getattr__(self, name):
        if name in (_[0] for _ in getmembers(UserEditable, predicate=isroutine)) and \
                self.is_content_editable:
            self._content_editable = True
            setattr(self, name, six.create_bound_method(
                six.get_unbound_function(getattr(UserEditable, name)), self))
            return getattr(self, name)
        else:
            raise AttributeError("Element '{}' has no attribute "
                                 "'{}'".format(self.__class__.__name__.capitalize(), name)) 

def _give_columns_django_field_attributes(self):
        """
        Add Django Field attributes to each cqlengine.Column instance.

        So that the Django Options class may interact with it as if it were
        a Django Field.
        """
        methods_to_add = (
            django_field_methods.value_from_object,
            django_field_methods.value_to_string,
            django_field_methods.get_attname,
            django_field_methods.get_cache_name,
            django_field_methods.pre_save,
            django_field_methods.get_prep_value,
            django_field_methods.get_choices,
            django_field_methods.get_choices_default,
            django_field_methods.save_form_data,
            django_field_methods.formfield,
            django_field_methods.get_db_prep_value,
            django_field_methods.get_db_prep_save,
            django_field_methods.db_type_suffix,
            django_field_methods.select_format,
            django_field_methods.get_internal_type,
            django_field_methods.get_attname_column,
            django_field_methods.check,
            django_field_methods._check_field_name,
            django_field_methods._check_db_index,
            django_field_methods.deconstruct,
            django_field_methods.run_validators,
            django_field_methods.clean,
            django_field_methods.get_db_converters,
            django_field_methods.get_prep_lookup,
            django_field_methods.get_db_prep_lookup,
            django_field_methods.get_filter_kwargs_for_object,
            django_field_methods.set_attributes_from_name,
            django_field_methods.db_parameters,
            django_field_methods.get_pk_value_on_save,
            django_field_methods.get_col,
        )
        for name, cql_column in six.iteritems(self._defined_columns):
            self._set_column_django_attributes(cql_column=cql_column, name=name)
            for method in methods_to_add:
                try:
                    method_name = method.func_name
                except AttributeError:
                    # python 3
                    method_name = method.__name__

                new_method = six.create_bound_method(method, cql_column)
                setattr(cql_column, method_name, new_method) 

def create_multi_node_evaluator(actual_evaluator, communicator):
    """Create a multi node evaluator from a normal evaluator.

    Actually this method patches the evaluator to work in multi node
    environment. This method adds several hidden attributes starting
    with `_mn_` prefix.

    Args:
        actual_evaluator: evaluator to be patched
            (e.g., ``chainer.training.extensions.Evaluator``)
        communicator: ChainerMN communicator

    Returns:
        The multi-node patched ``actual_evaluator``.

    .. note:: After patched, original evaluator does not work
              correctly in non-MPI environment.

    """

    actual_evaluator._mn_original_evaluate = actual_evaluator.evaluate
    actual_evaluator._mn_communicator = communicator

    def new_evaluate(self):
        local_mean_dict = self._mn_original_evaluate()

        # ChainerX support:
        # We need convert chainerx ndarray to Native array because
        #   (1) allreduce_obj is used to compute global mean values, since
        #       a simple allreduce operation cannot be applied in evaluation.
        #   (2) allreduce_obj calls mpi4py.allreduce, which pickles the object
        #   (3) chainerx.ndarray preserves CUDA device internally when pickled
        #   (4) An error will occur when an ndarray is unpickled in another
        #       process
        array0 = list(local_mean_dict.values())[0]
        xp = backend.get_array_module(array0)
        if xp == chx and array0.device.backend.name == 'cuda':
            # Results of evaluation is fairly small, so
            # the ndarray is transferred to CPU and allreduce()-ed.
            # NOTE: Matrices for evaluation are transferred to the host memory
            # and sent via MPI instead of NCCL. Although evaluation matrices
            # are small in most cases, this is a potential performance issue.
            local_mean_dict = {
                name: chx.to_numpy(value)
                for name, value in local_mean_dict.items()
            }

        global_mean_dict = {
            name:
            self._mn_communicator.allreduce_obj(
                value) / self._mn_communicator.size
            for name, value in sorted(local_mean_dict.items())
        }
        return global_mean_dict

    actual_evaluator.evaluate = six.create_bound_method(
        new_evaluate, actual_evaluator)
    return actual_evaluator