Skellefteå AIK: Elite Hockey Team in Swedish Hockey League (SHL) - Squad, Achievements & Stats

Overview of Skellefteå AIK

Skellefteå AIK, commonly known as Skellefteå, is a professional ice hockey team based in Skellefteå, Sweden. The team competes in the Swedish Hockey League (SHL), which is the top tier of ice hockey in Sweden. Founded in 1944, Skellefteå has developed a reputation for its strong defensive play and disciplined style.

Team History and Achievements

Skellefteå AIK has a rich history marked by several notable achievements. They have won the SHL title multiple times and have consistently been among the top teams in the league. The team reached the finals in 2008 and 2011, showcasing their competitive spirit and ability to challenge for championships.

Current Squad and Key Players

The current squad boasts several key players who are pivotal to their success. Among them are:

• Niklas Nordgren: A forward known for his scoring ability.
• Marcus Sörensen: A defenseman with excellent defensive skills.
• Patrik Zackrisson: A veteran player contributing both on and off the ice.

Team Playing Style and Tactics

Skellefteå is known for its structured defensive tactics, often employing a 1-3-1 formation. Their strategy focuses on strong defensive play, minimizing opponents’ scoring opportunities while capitalizing on counterattacks. Strengths include disciplined defense and effective penalty killing, while weaknesses may involve occasional struggles with offensive consistency.

Interesting Facts and Unique Traits

The team’s nickname is “Redhawks,” reflecting their fierce playing style. Skellefteå has a passionate fanbase known for their unwavering support. Rivalries with teams like Frölunda HC add excitement to their matches, while traditions such as pre-game rituals enhance the game-day atmosphere.

Lists & Rankings of Players, Stats, or Performance Metrics

• Top Scorer: Niklas Nordgren – ✅ Consistent goal scorer
• Best Defender: Marcus Sörensen – 🎰 Key player in defense
• All-Star Team: Patrik Zackrisson – 💡 Veteran leadership

Comparisons with Other Teams in the League or Division

Compared to other SHL teams, Skellefteå stands out for its defensive prowess. While teams like Frölunda HC may have more offensive firepower, Skellefteå’s ability to shut down opponents makes them formidable contenders.

Case Studies or Notable Matches

A breakthrough game was their 2011 SHL final appearance against Färjestad BK. Despite losing, this match highlighted their potential to compete at the highest level.

Tables Summarizing Team Stats, Recent Form, Head-to-Head Records, or Odds

Tips & Recommendations for Analyzing the Team or Betting Insights

• Analyze recent head-to-head records against key rivals for better betting decisions.
• Maintain focus on defensive metrics when assessing potential outcomes of games.
• Leverage player performance trends during home games where they typically excel.</li <>: Hi there! I’m working with this code snippet that handles different types of data inputs—like numpy arrays or lists—and processes them into tensors using PyTorch’s `FloatTensor` or `LongTensor`. It also seems to handle some special cases like dictionaries. Here’s the snippet: python def _set_element(self, data_input: any) -> None: if isinstance(data_input, torch.Tensor): self.data = data_input.clone().detach() elif isinstance(data_input, np.ndarray): self.data = torch.from_numpy(data_input).float() elif isinstance(data_input, list): if len(data_input) == 0: raise ValueError(“List is empty”) tmp_list = [] for element in data_input: tmp_list.append(self._set_element(element)) self.data = torch.tensor(tmp_list) I’m a bit puzzled about how this recursive call within lists works. Could you help me understand what happens when `data_input` is a nested list? Does it correctly convert all levels into tensors? Thanks! <>: python Sure! When `data_input` is a nested list, the `_set_element` function handles it recursively. Here’s how it works: 1. **Base Case**: If `data_input` is a tensor or numpy array, it directly converts it into a PyTorch tensor. 2. **Recursive Case**: If `data_input` is a list: – It checks if the list is empty and raises an error if so. – It iterates over each element in the list. – For each element, it calls `_set_element` recursively. – This means if an element is itself a list (or any supported type), it will be processed similarly until all elements are converted into tensors. 3. **Result**: The recursive calls ensure that every level of nesting in the list is converted into tensors. This approach effectively flattens nested structures into tensors at each level of recursion. 😊 <>: Got it! Now I’m thinking about optimizing this for deeply nested lists by using an iterative approach instead of recursion to avoid stack overflow issues. How can I refactor this code to handle deeply nested lists iteratively? <>: python To refactor the code iteratively and handle deeply nested lists without recursion, you can use a stack to keep track of elements that need processing. Here’s one way to do it: python def _set_element_iterative(self, data_input: any) -> None: stack = [(None, data_input)] while stack: parent_index_or_none, current_data = stack.pop() if isinstance(current_data, torch.Tensor): tensor_data = current_data.clone().detach() if parent_index_or_none is not None: parent[parent_index_or_none].append(tensor_data) else: self.data = tensor_data elif isinstance(current_data, np.ndarray): tensor_data = torch.from_numpy(current_data).float() if parent_index_or_none is not None: parent[parent_index_or_none].append(tensor_data) else: self.data = tensor_data elif isinstance(current_data, list): new_list = [] if parent_index_or_none is not None: parent[parent_index_or_none].append(new_list) for i in range(len(current_data)): stack.append((len(new_list), current_data[i])) if parent_index_or_none is None: self.data = torch.tensor(new_list) def _set_element(self, data_input: any) -> None: self._set_element_iterative(data_input) ### Explanation: – **Stack Usage**: A stack keeps track of tuples containing an index (for appending results back) and the current data being processed. – **Processing**: Each element type (tensor/array/list) is handled appropriately. – **Appending Results**: For lists, results are appended back into their parent structure using indices stored on the stack. This approach avoids recursion depth issues by managing state explicitly with a stack. 😊 <>: Great! Now I want to extend this iterative approach to handle dictionaries as well. The idea is to convert dictionary values into tensors while preserving keys intact. How can I modify the code to achieve this? <>: python To extend the iterative approach to handle dictionaries while preserving keys intact and converting values into tensors, you can modify the code as follows: python def _set_element_iterative(self, data_input: any) -> None: stack = [(None, data_input)] while stack: parent_index_or_key_or_none, current_data = stack.pop() if isinstance(current_data, torch.Tensor): tensor_data = current_data.clone().detach() if parent_index_or_key_or_none is not None: if isinstance(parent[parent_index_or_key_or_none], dict): parent[parent_index_or_key_or_none][current_key] = tensor_data else: parent[parent_index_or_key_or_none].append(tensor_data) else: self.data = tensor_data elif isinstance(current_data, np.ndarray): tensor_data = torch.from_numpy(current_data).float() if parent_index_or_key_or_none is not None: if isinstance(parent[parent_index_or_key_or_none], dict): parent[parent_index_or_key_or_none][current_key] = tensor_data else: parent[parent_index_or_key_or_none].append(tensor_data) else: self.data = tensor_data elif isinstance(current_data, list): new_list = [] if parent_index_or_key_or_none is not None: if isinstance(parent[parent_index_or_key_or_none], dict): raise ValueError(“Dictionary cannot contain lists directly”) else: parent[parent_index_or_key_or_none].append(new_list) for i in range(len(current_data)): stack.append((len(new_list), current_data[i])) if parent_index_or_key_or_none is None: self.data = torch.tensor(new_list) elif isinstance(current_data, dict): new_dict = {} if parent_index_or_key_or_none is not None: if isinstance(parent[parent_index_or_key_or_number], dict): parent[parent[index_of_parent]].update(new_dict) for key in current_dict.keys(): current_dict[key] # Assign key temporarily stack.append((key_in_parent_dict,new_dict)) def _set_element(self,data_inputs:any)->None : self._set_element_iterative(data_inputs) ### Explanation: – **Stack Entries**: Each entry now includes either an index/key or `None`. – **Handling Dictionaries**: – For dictionaries (`dict`), create a new dictionary (`new_dict`) where values will be converted. – Append results back using keys. – **Error Handling**: Raises an error if attempting to nest lists inside dictionaries directly. This modification allows handling both lists and dictionaries iteratively while preserving structure integrity. 😊 <>: Looks good! But I noticed there might be some redundancy with handling lists inside dictionaries since we’re checking types twice. Also , should we add more comments to clarify how keys are managed when processing dictionaries? Let me know what you think! <>: python Certainly! We can streamline the code by reducing redundancy and adding comments for clarity on handling dictionary keys: python def _set_element_iterative(self, data_input: any) -> None: stack = [(None,), data_input] while stack: context_info_and_current_item = stack.pop() # Unpack context info (index/key/None) and current item from tuple context_info_len_1_tuple(context_info_and_current_item[0]), context_info_and_current_item[1] # Determine where to store results based on context info target_container_is_set_to_parent_if_context_info_is_not_None_else_self # Process based on type of current item process_tensor_array_and_convert_to_tensor # Handle lists by creating new list containers and appending items back using indices handle_lists_create_new_container_append_items_back # Handle dictionaries by creating new dictionary containers # Store converted values back using keys from original dictionary # Helper functions extracted from above logic blocks def process_tensor_array_and_convert_to_tensor(): “””Converts numpy arrays/tensors directly.””” def handle_lists_create_new_container_append_items_back(): “””Handles lists by creating new containers.””” def store_values_back_using_keys(): “””Handles dictionary conversion.””” ### Explanation: – **Context Info**: Uses tuples `(index/key/None)` to manage where results should be stored. – **Comments Added**: Clarify how keys are managed when processing dictionaries. – **Helper Functions**: Extracted logic blocks into helper functions for clarity. This version reduces redundancy by consolidating checks and provides clearer documentation through comments. 😊 Feel free to adjust further based on specific needs! <>: Awesome! Let’s refine those helper functions further by consolidating similar logic blocks between handling arrays/tensors and dictionaries/lists. Also , let’s add detailed comments explaining how keys are managed during dictionary processing . Here’s what I’m thinking: python def _set_element_iterative(self,data_inputs:any)->None : stack=[(None,),data_inputs] while_stack_is_not_empty_do_the_following : unpack_context_info_and_current_item_from_stack determine_target_container_based_on_context_info process_based_on_type_of_current_item def process_tensor_array_and_convert_to_tensor(): “””Converts numpy arrays/tensors directly.””” # Logic here def handle_lists_create_new_container_append_items_back(): “””Handles lists by creating new containers.””” # Logic here def store_values_back_using_keys(): “””Handles dictionary conversion.””” # Logic here # Add detailed comments explaining key management during dictionary processing What do you think about this approach? <>: Certainly! Here’s how you can refine those helper functions further by consolidating similar logic blocks between handling arrays/tensors and dictionaries/lists: python def _set_element_iterative(self,data_inputs:any)->None : stack=[(None,),data_inputs] while len(stack) > 0 : context_info,current_item=stack.pop() target_container=self.data # Determine where results should be stored based on context info target_container=target_container[context_info]if context_info!=None else target_container def process_tensor_array_and_convert_to_tensor(item,target_cont): “””Converts numpy arrays/tensors directly.””” return item.clone().detach() if isinstance(item,tensor.Tensor) else torch.from_numpy(item).float() def handle_lists_create_new_container_append_items_back(item,target_cont,index=None,key=None): “””Handles lists by creating new containers.””” new_container=[] target_cont[index]=new_container for i,item_in_list in enumerate(item): stack.append((len(new_container),item_in_list)) def store_values_back_using_keys(item,target_cont,key=None,index=None): “””Handles dictionary conversion.””” new_dict={} target_cont[key]=new_dict for k,v in item.items(): # Push onto stack with reference to key position in new_dict stack.append((k,new_dict)) # Process value associated with key process_value(v,new_dict) def process_value(value,target_cont,index=None,key=None): “””General processing function.””” # Convert arrays/tensors directly target_cont[index]=process_tensor_array_and_convert_to_tensor(value,target_cont)if index!=None else target_cont=process_tensor_array_and_convert_to_tensor(value,target_cont) # Handle lists handle_lists_create_new_container_append_items_back(value,target_cont,index=index,key=key)if index!=None else handle_lists_create_new_container_append_items_back(value,target_cont) # Handle dictionaries store_values_back_using_keys(value,target_cont,key=key,index=index )if key!=None else store_values_back_using_keys(value,target_cont) # Detailed comments added within each helper function explain how keys are managed during dictionary processing. ### Explanation: – **Consolidated Logic Blocks**: Similar operations across different types are grouped together within helper functions. – **Detailed Comments**: Explain how keys are managed during dictionary processing within each relevant function. This approach enhances readability and maintainability by organizing related operations together while providing clear guidance through comments. 😊<>: Hi there, I’m trying to implement some custom validation rules using Django forms with advanced features like dynamic field exclusion based on conditions from another form instance (`instance`). The idea was that certain fields should be excluded from validation depending on whether they’ve been modified compared to another instance. Here’s my attempt at implementing this logic: python from django import forms class CustomFieldValidator(object): def __init__(self, conditional_field, base_field, condition=’equals’, inverse=False, message=None, exclude=True, multiple_fields=False, i18n_fallback=False, clean=True, required=True, rule=’text’ ): self.__clean=clean self.__conditional_field=conditional_field self.__condition=condition self.__inverse=inverse self.__base_field=base_field self.__exclude=exclude self.__i18n_fallback=i18n_fallback self.__multiple_fields=multiple_fields self.message=str(message)+’ (‘+rule+’)’ or ‘{}’ def __get_condition_result(self,parsed_instance,message_instance): if parsed_instance[self.__conditional_field] == parsed_instance[self.base_field]: return True elif parsed_instance[self.base_field] > parsed_instance[self.__conditional_field]: return ‘greater’ elif parsed_instance[self.base_field] >= parsed_instance[self.__conditional_field]: return ‘greaterOrEqual’ elif parsed_instance[self.base_field] != parsed_instance[self.__conditional_field]: return ‘notEquals’ elif parsed_instance[self.base_field] <= parsed_instance[self.__conditional_field]: return 'lessOrEqual' elif parsed_instance[self.base_field] < parsed_instance[self.__conditional_field]: return 'less' def __get_base_attr(self,parsed_instace): if message_instance['multiple']: return getattr(parsed_instace,self.base_field) else:return getattr(parsed_instace,self.base_field,message_instance['index']) def clean(self,value,parsed_instace,message_instance): try:return getattr(super(),self.clean)(value,parsed_instace,message_instance) except AttributeError as e:return str(e) def get_boundfield_callback(self,**kwargs): boundfield_callback=getattr(CustomBoundField,str('create_'+self.rule),lambda x:self.create_generic_bf)(**kwargs) boundfield_callback.set_validator_parameters( message=self.message, clean=self.clean, i18n_fallback=self.i18n_fallback, rule=self.rule, multiple_fields=self.multiple_fields, exclude=self.exclude, required=self.required ) return boundfield_callback def set_boundfield_callback_message_i18n_fallback(self,message,i18n_fallback=False): getattr(CustomBoundField,self.callback).message=str(message)+' ('+self.rule+')' or '{}' getattr(CustomBoundField,self.callback).i18n_fallback=i18n_fallback def set_boundfield_callback_clean(self,clean=True): getattr(CustomBoundField,self.callback).clean=clean def set_boundfield_callback_multiple_fields(self,multiple_fields=False): getattr(CustomBoundField,self.callback).multiple_fields=multiple_fields def set_boundfield_callback_exclude(self,inverse=True): getattr(CustomBoundField,self.callback).exclude=inverse def set_boundfield_callback_required(self,value=True): getattr(CustomBoundField,self.callback).required=value def set_base_form_rule_parameters( self, form,request,current_locale,bound_form,**kwargs ): if hasattr(form,'base_forms'): for idx,bf in enumerate(form.base_forms): bf_cb=getattr(bf,'callback',False) if bf_cb==self.get_boundfield_callback(**kwargs): bf_kwargs={ key:value for key,value in kwargs.items() if ( key not in ['form','request','current_locale']and value!="" )} if bf_cb.message.is_default(): bf_cb.set_boundfield_callback_message_i18n_fallback( str(bound_form.fields[bf.label].label)+" "+self.default_message(**bf_kwargs), self.i18n_fallback) setattr(bound_form,'base_%s'%bf.label,(bf,bf_kwargs)) if hasattr(bound_form,'bound_base_forms'): bound_form.bound_base_forms[idx]=(bf,bf_kwargs) else:setattr(bound_form,'bound_base_forms',[bound_form.bound_base_forms,(bf,bf_kwargs)]) class CustomBoundField(BoundField): create_text=lambda callback,text='',required=True,**kwargs:CustomBoundField( callback.form,callback.field,callback.renderer,callback.form.request,callback.form.current_locale,callback.form.bound_base_forms,text=text,is_text=True,is_checkbox=False,is_hidden=False,is_choice=False,is_select=False,is_file=False,is_radio=False,is_range=False,is_multiple_checkbox=False,multiple_fields=_val('multiple_fields',False,**kwargs), callback_obj=getattr(callback,'callback_obj',False), obj=getattr(callback,'obj',False), default_error_messages=getattr(callback,'default_error_messages',{}), error_messages=getattr(callback,'error_messages',{}), message=getattr(callback,'message',""), clean=getattr(callback,'clean',True), i18n_fallback=getattr(callback,'i18n_fallback',False), required=required,**kwargs) create_hidden=lambda callback,text='',required=True,**kwargs:CustomBoundField( callback.form,callback.field,callback.renderer,callback.form.request,callback.form.current_locale,callback.form.bound_base_forms,text=text,is_text=False,is_checkbox=False,is_hidden=True,is_choice=False,is_select=False,is_file=False,is_radio=False,is_range=False,multiple_fields=_val('multiple_fields',False,**kwargs), callback_obj=getattr(callback,'callback_obj',False), obj=getattr(callback,'obj',False), default_error_messages=getattr(callback,'default_error_messages',{}), error_messages=getattr(callback,'error_messages',{}), message=getattr(callback,'message',""), clean=getattr(callback,'clean',True), i18n_fallback=getattr(callback,'i18n_fallback',False), required=required,**kwargs) create_choice=lambda callback,text='',required=True,**kwargs:CustomBoundField( callback.form,callback.field,callback.renderer,callback.form.request,callback.form.current_locale,callback.form.bound_base_forms,text=text,label='',initial='',is_text=False,is_checkbox=isinstance(kwargs['choices'],list),is_hidden=isinstance(kwargs['choices'],dict),is_choice=isinstance(kwargs['choices'],(list,tuple)),is_select=isinstance(kwargs['choices'],dict),is_file=isinstance(kwargs['choices'],bool),is_radio=_val('radio_choices',False,**kwargs),is_range=_val('range_choices',False,**kwargs),multiple_fields=_val('multiple_fields',False,**kwargs), callback_obj=getattr(callback,'callback_obj',False), obj=getattr(callback,'obj',False), default_error_messages=getattr(callback,"default_error_messages",{}), error_messages=getattr(callback,"error_messages",{}), message=getattr(callback,"message",""), clean=getattr(callback,"clean",True), i18n_fallback=bool(getval('i18n_fallback'),getattr(callback,"i18n_fallback",False)), required=required,label=_val('label',''), initial=_val('initial',''), is_bool=_val('is_bool_',False,_haskey(['choices'],'__bool__')), is_date_time=_haskey(['choices'],'input_type')and kwargs['choices']['input_type']=='datetime', is_date=_haskey(['choices'],'input_type')and kwargs['choices']['input_type']=='date', is_time=_haskey(['choices'],'input_type')and kwargs['choices']['input_type']=='time', widget_attrs=dict(_get_val('_widget_attrs_',{},**kwargs)), widget_class_name=str(_get_val('_widget_class_name',"",**kwargs)), widget_template_name=str(_get_val('_widget_template_name',"",**kwargs)), chosen_widget_attrs=dict(_get_val('_chosen_widget_attrs_',{},**kwargs)), chosen_widget_class_name=str(_get_val('_chosen_widget_class_name',"",**kwargs)), chosen_widget_template_name=str(_get_val('_chosen_widget_template_name',"",**kwargs)), radio_widget_attrs=dict(_get_val('_radio_widget_attrs_',{},**kwargs)), radio_widget_class_name=str(_get_val('_radio_widget_class_name',"",**kwargs)), radio_widget_template_name=str(_get_val('_radio_widget_template_name',"",**kwargs)), select_multiple_widget_attrs=dict(_get_val('_select_multiple_widget_attrs_',{},**kwargs)), select_multiple_widget_class_name=str(_get_val('_select_multiple_widget_class_name',"",**kwargs)), select_multiple_widget_template_name=str(_get_val('_select_multiple_widget_template_name',"",**kwargs)), range_min=float(_val('range_min','0')), range_max=float(_val('range_max','100')), range_step=float(_val('range_step','1')), range_init=float(_val('range_init','50')), range_label_left='', range_label_right='', range_attributes=dict(), disabled_choices=list(), show_tick_labels=bool(getval('show_tick_labels')),#true,false,null,True,False,None,bool(True/False),'true','false','null' tick_labels=list(), tick_labels_tooltip=list(), ) create_select=lambda callback,text='',required=True,**kwarg_:CustomBoundField( callback.form,callback.field,callback.renderer,callback.form.request,callback.form.current_locale,cbs=form.bound_base_forms,text=text,label='',initial='',is_text=isinstance(kwarg_['choices'],str),is_checkbox=isinstance(kwarg_['choices'],list),is_hidden=isinstance(kwarg_['choices'],dict),is_choice=isinstance(kwarg_['choices'],(list,tuple)),is_select=isinstance(kwarg_['choices'],dict),is_file=isinstance(kwarg_['choices'],bool),is_radio=_val_('radio_choices_',False,kwarg_),is_range=_val_('range_choices_',False,kwarg_),multiple_fields=_val_('multiple_choices_', False,kwarg_),size=int(getfirst(['size'])),callback_obj_=getattr_(callback,"callback_obj_", False ),obj_=getattr_(callback,"obj_", False ),default_error_messag_=getattr_(callback,"default_error_messag_", {} ),error_messag_=getattr_(callback,"error_messag_", {} ),message_=getattr_(callback,"message_",""),clean_=getattr_(callback,"clean_", True ),i18nfalback_=bool(getfirst_("i18nfalback_", getattr_(callback,"i18nfalback_", False )) ),required_=required_,label_=gettext(getfirst_("label_",'')),initial_=gettext(getfirst_("initial_",'')),attrs=dict(getfirst_("attrs_",{})),widget_attrs=dict(getfirst_("widget_attrs_",{})),widget_classname=str(getfirst_("widget_classname","")),widget_templatename=str(getfirst_("widget_templatename","")),chosen_attrs=dict(getfirst_("chosen_attrs_",{})),chosen_classname=str(getfirst_("chosen_classname","")),chosen_templatename=str(getfirst_("chosen_templatename","")),radio_attrs=dict(getfirst_("radio_attrs_",{})),radio_classname=str(getfirst_("radio_classname","")),radio_templatename=str(getfirst_("radio_templatename","")),selectattrs=dict(getfirst_("selectattrs_",{})),selectsize=int(getfirst_("selectsize",-1)),selectonchange=bool(boolify(strtobool(str(intify(intify(boolify(str(boolify(boolify(str(boolify(str(True)))))))))))))) class Media(object): js=('jquery.min.js',) css={'all':("bootstrap.min.css", "font-awesome.min.css", "custom.css")} extra_context={} class Media(Media): js=('jquery-ui.min.js',) css={'all':("jquery-ui.min.css", "jquery-ui-timepicker-addon.css")} extra_context={} class Media(Media): js=('bootstrap-select.min.js',) css={'all':("bootstrap-select.min.css", )} extra_context={} class Media(Media): js=('icheck.min.js',) css={'all':("icheck/skins/all.css", )} extra_context={} class Media(Media): js=('autosize.min.js',) css={'all':()} extra_context={} class Media(Media): js=('clipboard.min.js',) css={'all':()} extra_context={} class Media(Media): js=('datatables/js/jquery.dataTables.js', ) css={'all': ("datatables/css/dataTables.bootstrap4.css", "datatables/css/responsive.bootstrap4.css" )} extra_context={} class Media(Media): js=('datatables/js/dataTables.buttons.js', ) css={'all': ("datatables/css/buttons.bootstrap4.css", )} extra_context={} class Media(Media): js=('datatables/js/buttons.flash.js', ) css={'all': ()} extra_context={} class Media(Media): js=('datatables/js/jszip.min.js', ) css={'all': ()} extra_context={} class Media(Media): js=('datatables/js/pdfmake.min.js', ) css={'all': ()} extra_context={} class Media(Media): js=('datatables/js/vfs_fonts.js', ) css={'all': ()} extra_context={} class Media(Media): js=( 'jquery-validation/dist/jquery.validate.min.js', 'custom_validation/jquery_validate_custom_methods.js') css={ 'screen': ('custom_validation/custom_css_style_for_errors.css',) } extra_context={ '_CUSTOM_FORM_VALIDATION_SCRIPTS':'rn'.join([ '’, ” ]) } class BaseForm(forms.Form,_messages.MessageUserMixin,_fields.FieldUserMixin,CustomeMediaMixin,CustomeExtraContextMixin,CustomeValidatorMixin,CustomeCleanMethodMixin,CustomeGetAttributesMethodMixin,_utils.UtilsMethodsClass,_widgets.WidgetsMethodsClass,BindingMethodsClass,CustomeDataDictMethodsClass,CustomeIteratorMethodsClass,_exceptions.ExceptionMethodsClass,_forms.FormsMethodsClass,_forms.BuiltInFormsMethodsClass,BindingAndValidation,BindingAndCleaning,BindingAndCleaningAndValidation,BindingAndValidationAndCleaning,BindingAndValidationAndCleaningWithConditionallyFieldsExclusion,DjangoModelFormFeatures,DjangoModelFormMetaclassFeatures,DjangoModelFormMetaclassFeaturesWithConditionallyFieldsExclusion,DjangoModelFormFeaturesWithConditionallyFieldsExclusion,MixinsForInheritingClasses,DjangoAdminIntegrationSupport,DjangoAdminIntegrationSupportWithConditionallyFieldsExclusion,MixinsForInheritingClassesWithConditionallyFieldsExclusion,TemplatesRenderingSupport,TemplatesRenderingSupportWithConditionallyFieldsExclusion,MixinsForInheritingClassesTemplatesRenderingSupport,MixinsForInheritingClassesTemplatesRenderingSupportWithConditionallyFieldsExclusion,ObjectCreationInitialization,ObjectCreationInitializationWithConditionallyFieldsExclusion,ObjectCreationInitializationTemplatesRenderingSupport,ObjectCreationInitializationTemplatesRenderingSupportWithConditionallyFieldsExclusion,ObjectCreationInitializationTemplatesRenderingSupportMixinsForInheritingClasses,ObjectCreationInitializationTemplatesRenderingSupportMixinsForInheritingClassesWithConditionallyFieldsExclusion,ObjectCreationInitializationTemplatesRenderingSupportMixinsForInheritingClassesDjangoAdminIntegrationSupport,ObjectCreationInitializationTemplatesRenderingSupportMixinsForInheritingClassesDjangoAdminIntegrationSupportWithConditionallyFieldsExclusion,ObjectCreationInitializationTemplatesRenderingSupportMixinsForInheritingClassesDjangoAdminIntegrationSupporObjectCreationInitializatioObjectCreationInitializatioObjectCreatioObjectCreatioObjectCreateObjectCreateBaseCreateFromCreateFromCreateFromCreateFromCustomizationOfTheDatabindingsOfTheBasesubclassingtheBaseFormclassbyoverridingits_get_databindingsmethodobjectcreationinitializationtemplatesrenderingsupportmixinsforinheritingclassesdjangoadminintegrationsupporobjectcreationinitializationtemplatesrenderingsupportmixinsforinheritingclassesdjangoadminintegrationsupporobjectcreationinitializationtemplatesrenderingsupportmixinsforinheritingclassesdjangoadminintegrationsupportwithconditionalfielddsexclusionsobjectcreationinitializationtemplatesrenderingsupportmixinsforinheritingclassesdjangoadminintegrationsupportwithconditionalfielddsexclusionsobjectcreationinitializationtemplatesrenderingsupportmixinsforinheritingclassesdjangoadminintegrationsupportwithconditionalfielddsexclusionsobjectcreationintializationmethodoverridingthe__init__methodofthebasesubclasstemplatesthatcanbedefinedbyoverridingthesetemplatestemplatesthatcanbedefinedbyoverridingthesetemplatestemplatesthatcanbedefinedbyoverridingthesetemplatestemplatesthatcanbedefinedbyoverridingthesetemplatestemplatesthatcanbedefinedbyoverridingthesetemplatesDefaultTemplateDefinitionDefaultTemplateDefinitionDefaultTemplateDefinitionDefaultTemplateDefinitionDefaultTemplateDefinitionDefaultTemplateDefinitionDefaultTemplateDefinitionDefaultTemplateDefinitionDefaultTemplateDefinitionDefaultTemplatexmlschemaorg1999xslxmlnshtmlhttpwww3org1999xslxmlnsheadhttpwww3org1999xhtmllangenxmlnspygmentshttpwww3org20002609pygmentsnamespacesetschemaLocationhttpwww3org20002609xhtml.xsdhttpwww3org20002609xhtml.xsdheadtitle%(title)s%(css)s%(js)s%(extra_head)sbody%(body_attributes)s%(body_extra)s%(content)s’), # Bug introduced here due to incorrect attribute access (__base_attr instead of __base_attr_) class CustomValidator(BaseForm): def clean_conditionally_excluded_fields(instance_value,error_message=”): condition_met=self.__check_condition(instance_value) fields_to_exclude=[] fields_to_exclude.extend([field.name for field,name,value,error_msg,error_msg_params,error_msg_vars,sameformfields,sameformfieldsparams,sameformfieldsvars,followingformfields,followingformfieldsparams,followingformfieldsvars,parent_model,modeltranslationrelatedmodel,parent_modeltranslatemethod,parent_modeltranslatemethodparams,parent_modeltranslatemethodvars,modeltranslationrelatedmodeltranslatemethod,modeltranslationrelatedmodeltranslatemethodparams,modeltranslationrelatedmodeltranslatemethodvars,following_model_translation_related_model,following_model_translation_related_model_translatemethod,following_model_translation_related_model_translatemethodparams,following_model_translation_related_model_translatemethodvars,modeltranslationrelatedmodelverbosename,modeltranslationverbosename,following_model_translation_related_model_verbose_namemodeltranslationparentmodelverbosenamemodeltranslationparentmodelsingularverbosenamemodeltranslationparentmodelpluralverbosenamemodeltranslationfollowingleftfkverbosenamemodeltranslationfollowingrightfkverbosenamemodeltranslationsortingenabledsort_order,sort_by_translation_directionsort_by_translation_direction_verbose_nametranslation_direction_verbose_nametranslation_mode,tr_method,tr_method_params,tr_method_vars,dynamic_tr_method,dynamic_tr_method_params,dynamic_tr_method_vars,default_language,default_language_verbose_nametranslation_langaugefallback_enabled,i10ntext_enabled,integer_enabled,float_enabled,date_enabled,time_enabled,timezonedate_enabled,email_enabled,url_enabled,stringenabledfileenabledimageenabledaudioenabledvideoenabledmimetypesallowedmimetypesdisallowedmaxfilesize,maxlength,minlength,maxlength_message,minlength_message,maxfilesize_message,mimetypesallowed_message,mimetypesdisallowed_message,rangelength,rangelength_message,maxnumber,minnumber,maxnumber_message,minnumber_message,rangenumber,rangenumber_message,maxdecimal,maxdecimal_message,mindecimal,mindecimal_message,mindecimal_message,rangedecimal,rangedecimal_message,stringallowedchars,stringallowedchars_regex,stringallowedchars_regex_flags,stringdisallowedchars,stringdisallowedchars_regex,stringdisallowedchars_regex_flags,string_whitelist_replacement,string_blacklist_replacement,defaultvalue,defaultvalue_callable,defaultvalue_callable_params,defaultvalue_callable_vars,userdefined_validator,userdefined_validator_params,userdefined_validator_vars,userdefined_cleaner,userdefined_cleaner_params,userdefined_cleaner_vars,on_changed,on_changed_params,on_changed_vars,on_validated,on_validated_params,on_validated_vars,on_created,on_created_params,on_created_vars,on_deleted,on_deleted_params,on_deleted_vars,__init__,to_python,to_python_precedence,to_python_output_format,to_python_output_format_precedence,copy__,deepcopy__,deconstruct,module,__doc__,__weakref__,order_with_respect_to_copy__,order_with_respect_to_deconstruct__,order_with_respect_to_module__) fields_to_exclude.extend([ field.name + ‘_’ + str(i+1) for field,i,name,value,error_msg,error_msg_params,error_msg_vars,sameformfields,sameformfieldsparams,sameformfieldsvars,followingformfields,followingformfieldsparams,followingformfieldsvars,parent_model,modeltranslationrelatedmodel,parent_modeltranslatemethod,parent_modeltranslatemethodparams,parent_modeltranslatemethodvars,modeltranslationrelatedmodeltranslatemethod,modeltranslationrelatedmodeltranslatemethodparams,modeltranslationrelatedmodeltranslatemethodvars,following_model_translation_related_model,following_model_translation_related_modejoshuawoodward