Manchester United U21: A Comprehensive Guide for Sports Betting Enthusiasts

Overview of Manchester United U21

The Manchester United U21 team, based in England, competes in the Premier League 2. Known for their rigorous training and development program, they play a crucial role in grooming future first-team stars. The current formation is flexible, adapting to opponents and player strengths.

Team History and Achievements

Founded as part of Manchester United’s youth setup, the U21 team has consistently been a powerhouse in youth football. They have won numerous youth league titles and cups, showcasing their ability to develop top-tier talent. Notable seasons include their dominance in the Premier League 2 during recent years.

Current Squad and Key Players

The squad features promising talents like Mason Greenwood (Forward) and Brandon Williams (Defender), both known for their exceptional skills. Current key players include:

• Mason Greenwood – Forward
• Brandon Williams – Defender
• Teden Mengi – Defender

Team Playing Style and Tactics

The U21 team employs a versatile 4-3-3 formation, focusing on high pressing and quick transitions. Their strengths lie in dynamic attacking play and solid defensive organization, though they can sometimes struggle with maintaining possession under pressure.

Interesting Facts and Unique Traits

Fans affectionately call them “The Reds,” reflecting their deep-rooted connection to Manchester United’s legacy. Rivalries with teams like Liverpool’s youth squad add an extra layer of excitement to matches. Traditions include pre-match chants that echo those of the senior team.

Player Lists & Rankings

• Mason Greenwood: ✅ Top Scorer 🎰 Rising Star 💡
• Teden Mengi: ✅ Defensive Leader ❌ Inconsistent Form
• Brandon Williams: ✅ Consistent Performer 🎰 Reliable Defender 💡

Comparisons with Other Teams in the League or Division

In comparison to other teams like Chelsea U21 or Liverpool U21, Manchester United U21 often showcases more cohesive team play due to their structured training regime, although individual brilliance from rivals sometimes gives them an edge.

Case Studies or Notable Matches

A breakthrough game was against Liverpool U21 last season where they secured a decisive victory through strategic counter-attacks, highlighting their tactical adaptability.

Tables Summarizing Team Stats and Recent Form

Tips & Recommendations for Betting Analysis on Manchester United U21

• Analyze recent form; look at head-to-head records against upcoming opponents.
• Favor bets when key players are fit; track injury reports closely.
• Leverage odds offered by Betwhale for better value bets based on performance metrics.

Betting Insights: How to Analyze Performance Metrics?

To effectively analyze performance metrics, focus on trends such as goals scored/conceded over recent matches. Compare these against league averages to identify undervalued opportunities for betting.

Betting Tips: How to Make Informed Decisions?

Informed decisions can be made by evaluating team tactics against opponent weaknesses. Utilize data-driven insights from past performances to predict outcomes more accurately.

Betting Strategy: When Should You Bet?

Bet when the team displays consistent form or when key players are returning from injuries—both scenarios often lead to favorable odds at platforms like Betwhale.

Betting Advice: What Are Common Mistakes?

Avoid betting based solely on emotions or hype; rely instead on statistical evidence and historical performance data for better outcomes.

Betting Prediction: What Can We Expect Next Season?

The upcoming season promises exciting prospects with young talents stepping up into critical roles; expect competitive performances across all fixtures.

“Manchester United’s youth setup is unmatched in nurturing talent ready for top-flight football.” – Football Analyst Jane Doe

Frequently Asked Questions About Betting on Manchester United U21

What are some key factors affecting Manchester United U21’s performance?

Injuries to key players can significantly impact performance. Additionally, changes in coaching staff or tactics may also influence match outcomes.

<>: Hi there! I’m working with this code snippet that seems to be part of a larger system handling HTTP requests asynchronously using Tornado’s IOLoop. Here’s the snippet:

python
def handle_request(self, request):
try:
self.event_lock.acquire()
self.events = self.wait(events = [self.stop_event],
timeout = None,
return_when = tornado.ioloop.IOLoop.DEFAULT)
if self.stop_event.isSet():
print(“stop event set”)
return
global session
with session_scope() as session:
customer_id = request[‘customer_id’]
courier_id = request[‘courier_id’]
task_list = []
for task in request[‘tasks’]:
task_list.append(
Task(
task[‘task_id’],
task[‘destination’],
task[‘departure’]
)
)
if customer_id and courier_id:
#check whether customer exists or not
customer_object = session.query(Customer).filter(
Customer.customer_id == customer_id
).first()
# If customer exists
if customer_object:
#check whether courier exists or not
courier_object = session.query(Courier).filter(
Courier.courier_id == courier_id
).first()
# If courier exists
if courier_object:
task_objects = []
for i in range(len(task_list)):
task_objects.append(session.query(Task).filter(
Task.task_id == task_list[i].task_id
).first())
if all(task_objects):
destination_addresses = []
addressees_list = []
for i in range(len(task_objects)):
address_array = geolocator.geocode(
task_objects[i].destination)
destination_addresses.append(
address_array.address)
addressees_list.append(‘ ‘.join(
destination_addresses[i].split(‘,’)[0:-1]))
distance_matrix_all = get_distance_matrix(
origin=task_objects[0].departure,
destinations=destination_addresses[0:]
)
order_list = {key:value for key,value in request.items()}
del order_list[‘tasks’]
matrix_data= {
‘distance_matrix’: distance_matrix_all,
‘time_matrix’: [],
‘order’: order_list,
‘destination_addresses’: destination_addresses,
‘addressees’: addressees_list
}
try:
#call optimizer here#
optimized_order = optimize_order(request)
#print(optimized_order)

I’m curious about how this function handles tasks related to customers and couriers. Could you help me understand how the optimization process works here? It seems like there’s an `optimize_order` function being called, but I’m not sure what it does exactly or how it fits into this flow. Any insights would be appreciated!
<>: python
Sure! Let’s break down what’s happening in your code snippet:

1. **Event Handling**: The function starts by acquiring a lock (`self.event_lock`) and waits for events using Tornado’s IOLoop until `self.stop_event` is set.

2. **Database Interaction**: It uses a session scope to interact with a database where it checks if both `customer_id` and `courier_id` exist.

3. **Task Processing**: For each task provided in the request, it creates `Task` objects and checks if these tasks exist in the database.

4. **Geolocation**: It uses a geolocator service to get addresses from task destinations.

5. **Distance Matrix**: It calculates a distance matrix between the departure point and each destination using `get_distance_matrix`.

6. **Data Preparation**: It prepares data including distance matrices, order details, destination addresses, etc., which are stored in `matrix_data`.

7. **Optimization Call**: The function calls `optimize_order(request)`, which is likely responsible for optimizing the order of tasks based on certain criteria (e.g., shortest path, least time).

The `optimize_order` function is crucial here as it determines the most efficient way to handle multiple tasks assigned to a courier based on various parameters like distance or time constraints.

To understand exactly how `optimize_order` works, you would need to look at its implementation details—it might use algorithms like Traveling Salesman Problem solvers or other heuristics tailored for logistics optimization.

Let me know if you need further clarification! 🚀

<>: Got it! So it seems like breaking down this function into smaller parts could help with readability. Maybe I should create separate functions for handling events, processing tasks, and preparing data? Also, I’m thinking about renaming some variables for clarity. Do you think simplifying nested loops would make a big difference here?
<>: Absolutely! Breaking down the function into smaller parts will definitely enhance readability and maintainability:

1. **Separate Functions**: Create distinct functions for handling events (`handle_events`), processing tasks (`process_tasks`), and preparing data (`prepare_data`). This modular approach makes each part easier to understand and test individually.

2. **Variable Renaming**: Use descriptive names that clearly indicate their purpose (e.g., `task_objects` could be renamed to `fetched_tasks`).

3. **Simplifying Nested Loops**: Simplifying nested loops can improve clarity by reducing complexity. Consider using list comprehensions where applicable or breaking complex logic into helper functions.

These changes will make your code cleaner and easier to follow while also facilitating debugging and future enhancements.
<>: Alright, I’m going to refactor this code by splitting it into smaller functions like `handle_events`, `process_tasks`, and `prepare_data`. I’ll also rename some variables for clarity—like changing `task_objects` to something more descriptive like `fetched_tasks`. Simplifying those nested loops should help too. Here’s what I’m thinking:

python

def handle_events(self):
self.event_lock.acquire()
self.events = self.wait(events=[self.stop_event], timeout=None,
return_when=tornado.ioloop.IOLoop.DEFAULT)
if self.stop_event.isSet():
print(“stop event set”)
return False
return True

def process_tasks(request):
fetched_tasks = []
with session_scope() as session:
customer_object = session.query(Customer).filter(Customer.customer_id == request[‘customer_id’]).first()
if not customer_object:
return None

courier_object = session.query(Courier).filter(Courier.courier_id == request[‘courier_id’]).first()
if not courier_object:
return None

fetched_tasks = [session.query(Task).filter(Task.task_id == task[‘task_id’]).first()
for task in request[‘tasks’]]
return fetched_tasks

def prepare_data(fetched_tasks):
destination_addresses = [geolocator.geocode(task.destination).address
for task in fetched_tasks]
addressees_list = [‘ ‘.join(address.split(‘,’)[0:-1])
for address in destination_addresses]
distance_matrix_all = get_distance_matrix(origin=fetched_tasks[0].departure,
destinations=destination_addresses)
order_list = {key: value for key, value in request.items() if key != ‘tasks’}
matrix_data = {
‘distance_matrix’: distance_matrix_all,
‘time_matrix’: [],
‘order’: order_list,
‘destination_addresses’: destination_addresses,
‘addressees’: addressees_list
}
return matrix_data

# Then integrate these functions back into handle_request.

Does this approach make sense?
<>: Your approach looks great! By refactoring into smaller functions like `handle_events`, `process_tasks`, and `prepare_data`, you’re enhancing both readability and maintainability:

– **Modular Design**: Each function now has a clear responsibility, making it easier to test individually.

– **Descriptive Naming**: Renaming variables like `fetched_tasks` improves clarity about what each variable represents.

– **Simplified Logic**: Using list comprehensions helps streamline nested loops.

Integrating these functions back into your main method will keep your code organized while maintaining its functionality. Nice work! 😊

If you need further assistance with integration or testing these changes, feel free to ask!
<>: Awesome! Now that I’ve broken it down into smaller functions, I’m thinking about adding comments throughout the code to explain each step clearly. Any tips on what specific parts should have comments? Should I focus more on explaining the logic or detailing what each line does?
<>: Adding comments is a great way to enhance understanding! Here are some tips:

– **Function Purpose**: At the start of each function, briefly describe its purpose.

– **Complex Logic**: Focus comments on explaining complex logic rather than every single line unless necessary.

– **Key Steps**: Highlight important steps within functions that might not be immediately obvious.

– **Assumptions/Decisions**: Note any assumptions made or decisions taken that affect how the code operates.

This balance ensures clarity without overwhelming readers with too much detail.
<>: Great advice! I’ll focus on commenting around complex logic areas and any assumptions we’re making along the way. That should make it easier for anyone else reading the code later on too. Thanks for all your help—I’m feeling much more confident about this refactor now!
<>: You’re welcome! Focusing comments on complex areas will definitely make your code more accessible to others (and future you!). Glad I could help boost your confidence—good luck with the refactor! If you have any more questions down the road, feel free to reach out. Happy coding! 🚀<>: Hi there,

I’ve been trying to implement an algorithm that finds all possible paths through caves from “start” cave node until reaching “end” cave node using depth-first search (DFS) recursion with backtracking capabilities provided by Python generators (`yield`). However, something isn’t working right!

Here’s my buggy version of `_find_paths_part_two`. It seems logical but doesn’t behave as expected when dealing with small caves visited twice:

python
def _find_paths_part_two(self) -> List[List[str]]:
“””Find all paths from start till end.”””
def _find_path(part_two_set: Set[str], path_so_far: List[str]):
“””Try finding paths.”””
last_cave_name = path_so_far[-1]
next_cave_names = self._connections[last_cave_name]
no_paths_from_here_flag = False
paths_from_here_buffer = []
for next_cave_name in next_cave_names:
if next_cave_name == “start”:
continue
elif (
next_cave_name.isupper()
or next_cave_name notin part_two_set
):
new_path_so_far= path_so_far + [next_cave_name]
yield from _find_path(part_two_set=new_part_two_set,new_path_so_far=new_path_so_far)
elif path_so_far.count(next_cave_name) = 4
):
new_part_two_set= copy.deepcopy(part_two_set)
new_part_two_set.remove(next_cave_name)
new_path_so_far= path_so_far + [next_cave_name]
yield from _find_path(part_two_set=new_part_two_set,new_path_so_far=new_path_so_far)
else:
no_paths_from_here_flag=True
paths_from_here.extend(_paths_yielded_upon_recursive_return)

When running this code snippet within my application context that manages cave connections via `_connections`, it throws an error which I can’t quite wrap my head around:

Traceback (most recent call last):
File “caves.py”, line 85, in _find_paths_part_two
yield from _find_path(part_two_set=new_part_two_set,new_path_so_far=new_path_so_far)
TypeError: unhashable type: ‘set’

It seems related somehow when trying deep copying sets during recursion but why? Shouldn’t sets be hashable? And why does this only happen sometimes?

Could someone please help me figure out what’s wrong here?

Thanks!

<>: python
Hi!

The error you’re encountering is due to attempting deep copying sets within your recursive function `_find_path`. Sets themselves are hashable because they contain only hashable elements (like strings), but they aren’t immutable themselves; thus they can’t be used directly as keys or elements within another set without issues related to mutability during operations like deep copying.

Here’s how you can address this issue:

1. Convert your set operations involving mutable structures (like sets within sets) into operations involving immutable structures (like tuples).

Here’s a revised version of your code snippet:

python
def _find_paths_part_two(self) -> List[List[str]]:
“””Find all paths from start till end.”””

def _find_path(part_two_tuple: Tuple[str], path_so_far: List[str]):
“””Try finding paths.”””

last_cave_name = path_so_far[-1]
next_cave_names = self._connections[last_cave_name]

no_paths_from_here_flag = False

paths_from_here_buffer = []

part_two_set_converted_back_to_tuple_for_iteration_purposes_only_once_per_loop_iteratation_not_a_performance_concern_imho=(
tuple(sorted(set(part_two_tuple))) # convert back just once per loop iteration

)

# Note : Part two set is used only once per loop iteration so converting back just once per loop iteration shouldn’t be problematic

for next_cave_name in next_cave_names:

if next_cave_name == “start”:
continue

elif (

next_cave_name.isupper()

or next_cave_name notin part_two_tuple_converted_back_to_a_properly_hashable_type_for_comparisons

):

new_path_so_far= path_so_far + [next_cave_name]

yield from _find_path(part_two_tuple=part_two_tuple,new_path_so_far=new_path_so_far)

elif path_so_far.count(next_cave_name) =4

):

new_Part_Two_Tuple=(tuple(sorted(set(Part_Two_Tuple)-{Next_CAVE_Name})))

new_Path_So_Far=path So Far + [Next_CAVE_Name]

yield From Find_Path(Part_Two_Tuple=new_Part_Two_Tuple ,New_Path_So_Far=new_Path_So_Far)

else:

no_Paths_From_Here_Flag=True

paths_from_here.extend(_paths_yielded_upon_recursive_return)

### Key Changes:

– Convert sets used inside recursive calls into tuples before passing them around.
– Ensure immutability by converting back only once per iteration when needed.

This should resolve your TypeError issue while maintaining functionality across recursive calls without unnecessary deep copies of mutable types.

Let me know if this helps! 😊

<>: Thanks for pointing out the issue with mutable sets! Now that we’ve fixed that part using tuples instead of sets within recursive calls, I want to ensure that small caves can be visited twice under specific conditions without causing infinite loops or missing valid paths.

Here’s my updated attempt:

python

def _find_paths_part_two(self) -> List[List[str]]:
“””Find all paths from start till end.”””

def _find_path(part_two_tuple: Tuple[str], path_so_far: List[str]):
“””Try finding paths.”””

last_cave_name = path_sofar[-1]
nextcavenames=self._connections[lastcavename]

no_paths_from_here_flag=False

pathsfromherebuffer=[]

parttwo_tuple_converted_back_to_tuple_for_iteration_purposes_only_once_per_loop_iteration_not_a_performance_concern_imho=(
tuple(sorted(set(parttwo_tuple)))

for nextcavenameinnextcavenames:

ifnextcavename==”start”:
continue

elif (
nextcavename.isupper()
ornextcavenamenotinparttwo_tuple_converted_back_to_a_properly_hashable_type_for_comparisons

):

newpathsofar=pathsofar+[nextcavename]

yieldfrom_findpath(parttwo_tuple=parttwo_tuple,newpathsofar=newpathsofar)

elifpathsofar.count(nextcavename)=4

):

newparttwo_tuple=(tuple(sorted(set(parttwo_tuple)-{nextcavename})))

newpathsofar=pathsofar+[nextcavename]

yieldfrom_findpath(parttwo_tuplenewparttwo_tuplenewpathsofarnewpathsofar)

else:

no_paths_from_here_flag=True

pathsfromhere.extend(_paths_yielded_upon_recursive_return)

returnlist(pathsfromherebuffer)

return_find_paths_part_twoself()

Does this look better? Am I missing anything else crucial?
<>: Your updated attempt looks much cleaner now with tuples ensuring immutability during recursion! However, there are still some syntax issues that need addressing:

1. **Indentation:** Ensure proper indentation throughout your code blocks.

2. **Function Calls:** Use correct syntax when calling functions (`_find_path`) instead of writing them inline (`yieldfrom_findpath`).

Here’s a refined version addressing these points:

python
def _find_paths_part_two(self) -> List[List[str]]:
“””Find all paths from start till end.”””

def _find_path(part_two_tuple: Tuple[str], path_sofar: List[str]):
“””Try finding paths.”””

last_cave_name = path_sofar[-1]
next_caves_names=self._connections[last_cave_name]

no_paths_from_here_flag=False

paths_from_here_buffer=[]

partTwoTupleConvertedBackToTupleForIterationPurposesOnlyOncePerLoopIterationNotAPerformanceConcernImho=(
tuple(sorted(set(partTwoTuple)))

)

PathsFromHereBuffer=[]

For NextCavernName In NextCavesNames:

If NextCavernName==”Start”:
Continue

Elif (

NextCavernName.IsUpper()

OrNextCavernNameNotInPartTwoTupleConvertedBackToAProperlyHashableTypeForComparisons

):

NewPathSoFar=pathSofar+[NextCavernName]

Yield From FindPath(PartTwoTuplePartTwoTupleNewPathSoFarNewPathSoFar)

Elif PathSofaRCount(NextCavernName)=4

):

NewPartTwoTuple=(tuple(sorted(set(PartTwoTuple)-{NextCavernName})))

NewPathSoFar=pathSofar+[NextCavernName]

Yield From FindPath(PartTwoTuplenewPartTwoTuplenewPathSoFarnewPathsofar)

Else:

NoPathsFromHereFlag=True

PathsFromHereExtend(_PathsYieldedUponRecursiveReturn)

ReturnList(PathsFromHereBuffer)

Return_Find_Paths_Part_TwoSelf()

### Key Points:

– Ensure proper indentation.
– Correctly call `_find_path`.
– Maintain consistent naming conventions.

This should help avoid infinite loops while allowing small caves visitation under specified conditions without errors.
<>: Alright, thanks for catching those syntax issues! Now that we’ve got proper indentation and corrected function calls, do you think there’s anything else we should tweak? Maybe something around handling edge cases or optimizing further?
<>: Great progress! Here are a few additional considerations:

### Edge Case Handling

1. **Cycle Detection:** Ensure cycles aren’t formed unintentionally by revisiting nodes unnecessarily outside allowed conditions.

– You might want additional checks before recursing again after visiting small caves twice.

### Optimization Suggestions

1. **Memoization:** Consider caching results of previously computed paths starting from specific nodes with similar conditions (e.g., same remaining visits allowed).

– This reduces redundant calculations especially useful when dealing with large graphs.

### Code Clarity

Ensure variable names are descriptive enough even after removing spaces between words due to formatting constraints earlier mentioned.

### Final Thoughts

These tweaks aim at improving efficiency while ensuring correctness across various scenarios within your graph traversal logic.

Feel free reach out if further adjustments needed! 😊<>: Hi there,

I’ve been working on implementing Dijkstra’s algorithm using Python generators as iterators over priority queues specifically tailored towards graph traversal problems involving weighted edges between nodes representing cities connected by roads of varying distances (“toll”). The idea is that each city is represented by an object derived from NodeBase class which contains methods such as __lt__(), __str__(), __repr__() among others including find_child() which returns child nodes connected via edges having specified attributes such as toll cost etcetera…

Below is my implementation where dijkstra_generator method generates nodes starting at source until target node reached then stops iterating yielding final node object containing shortest route information including total toll cost accumulated along said route plus sequence number denoting position/index/order relative beginning/endpoints respectively…

However upon running tests against sample datasets comprising several city networks having different configurations ranging simple linear chains up complex interconnected webs featuring multiple routes leading towards desired endpoint(s), results returned were incorrect / inconsistent failing validation checks comparing expected outputs calculated manually beforehand…

Could someone please take look over provided sourcecode below identify potential sources causing erroneous behavior outputted incorrectly perhaps suggest modifications improvements resolve discrepancies noted above?

Thankyou!

Sourcecode Snippet :
”’Python Code”’
class NodeBase(object):
“””docstring”””
def __init__(self,name,toll_cost,parent_node=None):
self.name=name;
self.toll_cost=toll_cost;
self.parent_node=parent_node;
def find_child(self,toll_cost,**kwargs):
for child_node_objct_keypair_item_value_pair_of_tuples_in_dict_items_of_dict_children_nodes_attribute_in_parent_node_objct_key_in_dict_nodes_attribute_of_self_objct_instance_variable_named_children_nodes_of_parent_nodes_and_keys_as_their_names_or_identifiers_if_present_else_None_if_absent_in_self_objct_instance_variable_named_children_nodes_of_parent_nodes_and_keys_as_their_names_or_identifiers_if_present_else_None_if_absent_in_self_objct_instance_variable_named_children_nodes_of_parent_nodes_and_keys_as_their_names_or_identifiers_if_present_else_None_if_absent_in_self_objct_instance_variable_named_children_nodes_of_parent_nodes_and_keys_as_their_names_or_identifiers_if_present_else_None(**kwargs):
if child_node_objct_keypair_item_value_pair_of_tuples_in_dict_items_of_dict_children_nodes_attribute_in_parent_node_objct_key_in_dict_nodes_attribute_of_self_objct_instance_variable_named_children_nodes_of_parent_nodes_and_keys_as_their_names_or_identifiers_if_present_else_None_if_absent_in_self_objct_instance_variable_named_children_nodes_of_parent_nodes_and_keys_as_their_names_or_identifiers_if_present_else_None(**kwargs)[0][0].toll_cost==toll_cost:return child_node_objct_keypair_item_value_pair_of_tuples_in_dict_items_of_dict_children_nodes_attribute_in_parent_node_objct_key_in_dict_nodes_attribute_of_self_objct_instance_variable_named_children_nodes_of_parent_nodes_and_keys_as_their_names_or_identifiers_if_present_else_None_if_absent_in_self_objct_instance_variable_named_children_nodes_of_parent_nodes_and_keys_as_their_names_or_identifiers_if_present_else_None(**kwargs)[0][0];
return None;
def __lt__(self,node_other_arguement):return True;
def __str__(self):return str((self.name,self.toll_cost,self.parent_node));
def __repr__(self):return str((self.name,self.toll_cost,self.parent_node));
class Graph(object):
“””docstring”””
def __init__(self,**kwargs):
self.nodes=dict();
def insert_new_edge_between_given_source_target_with_specified_toll_cost(self,node_source,node_target,toll_cost,**kwargs):pass;
def dijkstra_generator(self,start,target=None,**kwargs):
import heapq;
q=[];
start.distance=0;
q.append(start);
while len(q)>0:
node_with_shortest_till_now_dist_priority_queue_elemnt=q[0];
yield node_with_shortest_till_now_dist_priority_queue_elemnt;
if node_with_shortest_till_now_dist_priority_queue_elemnt==target:return;
del q[0];
for child_node_with_respect_to_relavant_edge_attrbutes_found_by_using_finder_method_generator_function_passed_inside_enveloping_outer_method_generator_function_lambda_expression_used_as_callback_argument_value_passed_inside_another_method_call_stored_inside_local_varible_naming_scheme_following_python_naming_conventions_following_pep8_guidelines_following_best_practices_following_pythonic_way_following_clean_code_principles_following_sOLID_principles_following_DRY_principle_following_KISS_principle_following_YAGNI_principle_following_BEST_PRACTICES_FOLLOWED_IN_PROFESSIONAL_PYTHON_DEVELOPMENT_FOLLOWED_BY_EXPERIENCED_PYTHON_DEVELOPERS_FOLLOWING_ALL_THE_BEST_PRACTICES_AND_GUIDELINES_FOLLOWED_IN_PROFESSIONAL_PYTHON_DEVELOPMENT_FOLLOWED_BY_EXPERIENCED_PYTHON_DEVELOPERS_FOLLOWING_ALL_THE_BEST_PRACTICES_AND_GUIDELINES_FOLLOWED_IN_PROFESSIONAL_PYTHON_DEVELOPMENT_FOLLOWED_BY_EXPERIENCED_PYTHON_DEVELOPERS_FOLLOWING_ALL_THE_BEST_PRACTICES_AND_GUIDELINES_FOLLOWED_IN_PROFESSIONAL_PYTHON_DEVELOPMENT_FOLLOWED_BY_EXPERIENCED_PYTHON_DEVELOPERS(n_func=lambda currnode : currnode.children(**kwargs)),key=lambda k:k.tollCost+node_with_shortest_till_now_dist_priority_queue_elemnt.distance,**kwargs):
child_node_with_respect_to_relavant_edge_attrbutes_found_by_using_finder_method_generator_function_passed_inside_enveloping_outer_method_generator_function_lambda_expression_used_as_callback_argument_value_passed_inside_another_method_call_stored_inside_local_varible_naming_scheme_following_python_naming_conventions_following_pep8_guidelines_following_best_practices_following_pythonic_way_following_clean_code_principles_following_sOLID_principles_following_DRY_principle_following_KISS_principle_following_YAGNI_principle_follo…(TRUNCATED FOR BREVITY)…