Espoon Tikka: Premier Team Profile - Squad, Stats & Achievements

Overview of Espoon Tikka

Espoon Tikka is a prominent football team based in Espoo, Finland. Competing in the Finnish Veikkausliiga, the team has established itself as a formidable force within the league. Founded in 1957, Espoon Tikka plays its home matches at the Tapiolan Urheilupuisto stadium. Under the guidance of their current coach, they employ a dynamic playing style that emphasizes both offensive prowess and defensive solidity.

Team History and Achievements

Espoon Tikka boasts a rich history filled with notable achievements. The club has won several Finnish championships and cup titles, marking them as one of the most successful teams in Finland. Notable seasons include their championship wins and strong performances in European competitions, which have solidified their reputation on both national and international stages.

Current Squad and Key Players

The current squad of Espoon Tikka features a blend of experienced veterans and promising young talents. Key players include:

• John Doe – Striker, known for his goal-scoring ability.
• Jane Smith – Midfielder, renowned for her playmaking skills.
• Alex Johnson – Defender, celebrated for his defensive acumen.

Team Playing Style and Tactics

Espoon Tikka typically employs a 4-3-3 formation, focusing on high pressing and quick transitions. Their strengths lie in their fast-paced attack and solid defensive organization. However, they occasionally struggle with maintaining possession under pressure.

Interesting Facts and Unique Traits

Espoon Tikka is affectionately known as “The Eagles,” reflecting their fierce competitive spirit. The team has a passionate fanbase known for their unwavering support. Rivalries with local clubs add an extra layer of excitement to their matches.

Lists & Rankings of Players, Stats, or Performance Metrics

• ✅ Top Scorer: John Doe – 15 goals this season
• ❌ Lowest Performer: Mark Brown – Struggling with form
• 🎰 Player to Watch: Lisa White – Rising star with potential impact
• 💡 Statistical Highlight: Average possession rate – 58%

Comparisons with Other Teams in the League or Division

Espoon Tikka is often compared to top-tier teams like HJK Helsinki due to their consistent performances. While they share similarities in tactical approaches, Espoon Tikka’s emphasis on youth development sets them apart.

Case Studies or Notable Matches

A breakthrough game for Espoon Tikka was their stunning victory against FC Inter Turku in the 2021 season finale, securing them a spot in European competitions. This match highlighted their resilience and tactical adaptability.

Statistic	Espoon Tikka	Rival Team A
Recent Form (Last 5 Games)	W-W-L-W-W	L-W-D-L-L
Head-to-Head Record (Last 10 Games)	6W-3D-1L
Odds for Next Match Win/Loss/Draw	1.8/3.5/3.0

Tips & Recommendations for Analyzing the Team or Betting Insights

To maximize betting potential on Espoon Tikka:

• Analyze recent form trends to gauge momentum.
• Closely monitor key player performances and injuries.</li
• Evaluate head-to-head records against upcoming opponents.

Frequently Asked Questions (FAQ)

What is Espoon Tikka’s current league position?

Espoon Tikka currently sits at 4th place in the Veikkausliiga standings.

Who are some key players to watch?

Jane Smith and John Doe are pivotal players whose performances significantly impact match outcomes.

How does Espoon Tikka fare against top teams?

Their head-to-head record shows competitiveness against top-tier teams, often resulting in closely contested matches.

“Espoon Tikka’s blend of experience and youthful energy makes them unpredictable opponents,” says football analyst Mark Thompson.

The Pros & Cons of Espoon Tikka’s Current Form or Performance

• ✅ Pro: Strong attacking lineup capable of scoring multiple goals per match.</li
• ❌ Con: Defensive lapses can lead to conceding late goals under pressure.

</ul

A Step-by-Step Guide to Understanding Espoon Tikka’s Tactics & Betting Potential:

1. Analyze previous matches to identify tactical patterns.
   </li
2. Evaluate player roles within the formation.
   </li
3. Cross-reference performance metrics with betting odds.
   </l[0]: import logging
   [1]: from collections import defaultdict

   [2]: from django.db.models import Q

   [3]: from data_refinery_common.logging_utils import get_and_configure_logger
   [4]: from data_refinery_common.models import (
   [5]: AnalysisJob,
   [6]: Biosample,
   [7]: Dataset,
   [8]: ExperimentSampleRelationship,
   [9]: ExperimentType,
   [10]: Organism,
   [11]: ProcessedFile,
   [12]: )
   [13]: from data_refinery_common.utils import select_prefixed_name_from_query_params

   [14]: logger = get_and_configure_logger(__name__)

   [15]: def get_dataset_experiments(dataset):
   [16]: """
   [17]: Get all experiments associated with this dataset.

   [18]: :param dataset:

   [19]: :return:

   [20]: """

   [21]: experiments = set()

   [22]: # Add all experiments directly associated with this dataset

   [23]: # TODO Remove once we've transitioned away from using ExperimentSampleRelationships
   [24]: experiment_sample_relationships = ExperimentSampleRelationship.objects.filter(
   [25]: biosample__dataset=dataset)

   [26]: for esr in experiment_sample_relationships:

   [27]: if esr.experiment:

   [28]: experiments.add(esr.experiment)

   ***** Tag Data *****
   ID: 1
   description: Function `get_dataset_experiments` retrieves all experiments associated
   with a given dataset by querying `ExperimentSampleRelationship` objects.
   start line: 15
   end line: 28
   dependencies:
   – type: Class
   name: ExperimentSampleRelationship
   start line: 8
   end line: 8
   context description: This function filters `ExperimentSampleRelationship` objects based
   on whether they are linked to a specific dataset through `biosample__dataset`. It's
   part of transitioning away from using `ExperimentSampleRelationships`.
   algorithmic depth: 4
   algorithmic depth external: N
   obscurity: 4
   advanced coding concepts: 4
   interesting for students: 5
   self contained: Y

   *************
   ## Suggestions for complexity

   1. **Asynchronous Query Execution**: Modify the code so that it performs asynchronous queries to fetch related experiments using Django’s async ORM capabilities.

   2. **Caching Mechanism**: Implement an advanced caching mechanism that caches results based on unique dataset identifiers to reduce database hits.

   3. **Data Validation**: Integrate complex data validation logic before adding experiments to ensure that only valid relationships are processed.

   4. **Logging Enhancements**: Add detailed logging mechanisms that log each step along with execution time metrics while fetching relationships.

   5. **Parallel Processing**: Use parallel processing techniques (e.g., threading or multiprocessing) to handle large datasets more efficiently by splitting tasks across multiple processors.

   ## Conversation

   [SNIPPET]

   This looks like it’s designed to filter `ExperimentSampleRelationship` objects linked via `biosample__dataset`. What would you like help with specifically?

   Add async query execution.

   `asyncio` can be combined with Django’s async ORM capabilities:

   python
   from django.db.models import Prefetch

   async def get_dataset_experiments(dataset):
   “””
   Get all experiments associated with this dataset.

   :param dataset:

   :return:

   “””
   experiments = set()

   # Asynchronously fetch related experiment sample relationships
   experiment_sample_relationships = await ExperimentSampleRelationship.objects.filter(
   biosample__dataset=dataset).prefetch_related(Prefetch(‘experiment’)).all()

   for esr in experiment_sample_relationships:
   if esr.experiment:
   experiments.add(esr.experiment)

   You’ll need Django >= 3.1 which supports async views out-of-the-box.

   Add detailed logging mechanism.

   `logging` module can be used:

   python
   import logging

   logger = logging.getLogger(__name__)

   async def get_dataset_experiments(dataset):
   “””
   Get all experiments associated with this dataset.

   :param dataset:

   :return:

   “””
   logger.info(“Starting query for dataset %s”, dataset.id)

   start_time = time.time()
   experiments = set()

   experiment_sample_relationships = await ExperimentSampleRelationship.objects.filter(
   biosample__dataset=dataset).prefetch_related(Prefetch(‘experiment’)).all()

   logger.info(“Fetched %d relationships”, len(experiment_sample_relationships))

   for esr in experiment_sample_relationships:
   if esr.experiment:
   logger.debug(“Adding experiment %s”, esr.experiment.id)
   experiments.add(esr.experiment)

   end_time = time.time()
   logger.info(“Completed query in %.2f seconds”, end_time – start_time)

   return experiments

   This adds logs at different levels (`info`, `debug`). Make sure your logging configuration captures these appropriately.

   Add complex data validation logic before adding experiments.

   `pydantic` can be used for robust validation:

   python
   from pydantic import BaseModel, ValidationError

   class ExperimentValidationModel(BaseModel):
   id: int
   name: str

   def validate_experiment(experiment):
   try:
   validated_data = ExperimentValidationModel(id=experiment.id, name=experiment.name)
   return validated_data.id is not None
   except ValidationError as e:
   logger.error(“Validation error occurred:”, e.json())
   return False

   async def get_dataset_experiments(dataset):
   “””
   Get all experiments associated with this dataset.

   :param dataset:

   :return:

   “””

   logger.info(“Starting query for dataset %s”, dataset.id)

   start_time = time.time()
   experiments = set()

   experiment_sample_relationships = await ExperimentSampleRelationship.objects.filter(
   biosample__dataset=dataset).prefetch_related(Prefetch(‘experiment’)).all()

   logger.info(“Fetched %d relationships”, len(experiment_sample_relationships))

   for esr in experiment_sample_relationships:
   if esr.experiment and validate_experiment(esr.experiment):
   logger.debug(“Adding validated experiment %s”, esr.experiment.id)
   experiments.add(esr.experiment)

   end_time = time.time()
   logger.info(“Completed query in %.2f seconds”, end_time – start_time)

   return experiments

   This validates each experiment before adding it using Pydantic models.<|end|
   <>: Hi there! I’m working on implementing an LRU cache using an OrderedDict-based doubly linked list approach as described here [SNIPPET]. The idea is that when I access an element by key, it should move that element to the front of the list since it’s now considered ‘hot’. However, I’m running into an issue where sometimes accessing an item doesn’t seem to update its position correctly.

   Here’s my modified code snippet:

   python
   class LRUCache(OrderedDict):
   def __init__(self, maxsize=128):
   OrderedDict.__init__(self)
   self.maxsize=maxsize

   def __getitem__(self,key,default=None):
   try:
   value=self.pop(key) # Incorrectly popping instead of moving key-value pair.
   self[key]=value # Reinserting key-value pair which disrupts order.
   return value
   except KeyError:
   return default

   def __setitem__(self,key,value):
   if key==self.keys()[0]and self.maxsize==len(self):# Bug here; incorrect check leading KeyError.
   self.popitem(last=False)
   if key in self.keys():
   self.pop(key)
   else:
   if len(self)>self.maxsize:
   self.popitem(last=False)
   super().__setitem__(key,value)

   And here’s the traceback I’m getting when I run my tests:

   Traceback (most recent call last):
   File “test_lru.py”, line 23, in test_access_order_maintenance
   cache[‘a’] # Accessing ‘a’ should move it but raises KeyError instead.
   KeyError Traceback (most recent call last)
   File “lru_cache.py”, line XX, in __getitem__
   value=self.pop(key)
   KeyError Traceback (most recent call last)
   File “test_lru.py”, line XX, in test_access_order_maintenance

   KeyError: ‘a’

   I’m confused about why accessing an existing key sometimes leads to a KeyError instead of updating its position correctly within the cache order? Any insights would be greatly appreciated!
   <>: python
   It looks like there are a couple of issues causing your LRU cache not to behave as expected.

   1. **Incorrect Popping Logic**: In your `__getitem__` method, you’re using `pop(key)` which removes the item completely before reinserting it back into the cache. Instead, you should use `move_to_end(key)` provided by `OrderedDict`, which moves an existing item to the end without removing it first.

   Here’s how you can modify your `__getitem__` method:

   python
   def __getitem__(self,key,default=None):
   try:
   value=self[key]
   self.move_to_end(key) # Correctly move key-value pair without removing it.
   return value
   except KeyError:
   return default

   2. **Incorrect Check Logic**: In your `__setitem__` method, checking if `key == self.keys()[0]` can lead to issues because converting keys to a list every time isn’t efficient or reliable when dealing with ordered dictionaries.

   Instead of checking keys directly like this:

   python
   if key == self.keys()[0] and self.maxsize == len(self):

   You might want to ensure you’re managing capacity correctly by just checking if you need eviction when adding new items:

   python
   def __setitem__(self,key,value):
   if key in self:
   self.pop(key) # Remove existing item before reinsertion.
   if len(self) >= self.maxsize:
   self.popitem(last=False) # Evict least recently used item.
   super().__setitem__(key,value) # Insert new item at end.

   These changes should help maintain the correct order when accessing elements without raising unexpected KeyErrors. Let me know if this resolves your issue! 😊

   <>: Thanks for pointing those out! Now I’m thinking about handling concurrency issues since multiple threads might access or modify the cache simultaneously. How can I make sure that operations on the cache are thread-safe? Should I use locks or some other mechanism?
   <>: To ensure thread safety when multiple threads access or modify your LRU cache simultaneously, you can use locks from Python’s `threading` module. Here’s how you can implement it:

   1. **Import Lock**: First, import `Lock` from the `threading` module.

   python
   from threading import Lock

   2. **Initialize Lock**: Add a lock attribute during initialization.

   python
   class LRUCache(OrderedDict):
   def __init__(self, maxsize=128):
   super().__init__()
   self.maxsize = maxsize
   self.lock = Lock() # Initialize lock here.

   3. **Use Lock Around Critical Sections**: Wrap critical sections where shared resources are accessed or modified with lock acquire/release operations.

   python
   def __getitem__(self,key,default=None):
   with self.lock: # Acquire lock before accessing shared resource.
   try:
   value=self[key]
   self.move_to_end(key)
   return value
   except KeyError:
   return default

   def __setitem__(self,key,value):
   with self.lock: # Acquire lock before modifying shared resource.
   if key in self:
   self.pop(key)
   if len(self) >= self.maxsize:
   self.popitem(last=False)
   super().__setitem__(key,value)

   Using locks ensures that only one thread can execute these critical sections at a time, preventing race conditions and ensuring consistency across threads.

   Let me know if you need further assistance! 🛡️😊<>: Hey there,

   I’ve been trying to implement some advanced mathematical operations involving matrix exponentiation within my neural network model class called `_MHA`. The idea is that these methods will allow me to manipulate matrices more effectively during training iterations.

   Here’s what I have so far:

   python
   import numpy as np

   class _MHA:

   @staticmethod
   def adjoint(matrix):
   return np.transpose(matrix.conj())

   @staticmethod
   def composition(matrix_a , matrix_b):
   return np.matmul(matrix_a , matrix_b)

   @staticmethod
   def tensor_product(matrix_a , matrix_b):
   m_a_rows , m_a_cols = matrix_a.shape
   m_b_rows , m_b_cols = matrix_b.shape
   manga_Arr_Buf_00=np.zeros(shape=(m_a_rows*m_b_rows,m_a_cols*m_b_cols),dtype=np.complex)
   for r_idx_in_A,r_idx_in_A_buf , r_idx_in final_matrix in zip(range(m_a_rows),range(0,m_a_rows*m_b_rows,m_b_rows),range(manga_Arr_Buf_00.shape)[0]):
   manga_Arr_Buf_00[r_idx_in_A_buf:r_idx_in_A_buf+m_b_rows,column_idx_00:m_column_idx_00+m_b_cols]=matrix_b*matrix_a[r_idx_in_A,r_column_idx_00]
   return manga_Arr_Buf_00

   @staticmethod
   def Kronecker_product(matrix_a , matrix_b):
   m_a_row , m_a_column=matrix_a.shape
   m_b_row , m_b_column=matrix_b.shape
   manga_Arr_Buf_00=np.zeros(shape=(m_a_row*m_b_row,m_a_column*m_b_column),dtype=np.complex)
   for r_idx_in_A,r_idx_in_A_buf,row_idx_n_final_matrix,column_idx_00,column_idx_00_buf,column_idx_n_final_matrix,in zip(range(m_a_row),range(0,m_agha_Row_M_B_Row,m_bgja_Row),range(manga_Arr_Buf_00.shape)[0],range(m_agha_Column),range(0,m_agha_Column_M_bgja_Column,m_bgja_Column),range(manga_Arr_Buf_00.shape)[1]):
   manga_Arr_Buf_00[r_index_in_A_buffer:r_index_in_A_buffer+mbgja_Row,column_index_C_buffer:C_Index_N_Final]=mbgja*ma_[row_index_In_A,column_index_C]
   return manga_arr_buf_00

   @staticmethod
   def exponential_MX(N_Mx,Mx):
   exp_mxt @+=np.eye(N_Mx,dtype=np.complex)
   temp_mxt @+=Mx.copy().astype(np.complex)
   factorial @+=1.
   for i_cop_i In range(1;101):
   exp_mx +=(temp_mx/i_cop_i)
   temp_mx @*=Mx.copy()
   factorial *=i_cop_i+1
   return exp_mx

   @staticmethod
   def sinmx(Mx):
   sin_mx+=np.zeros(shape=Mx.shape,dtype=np.complex)
   cos_mxt+=np.eye(Mx.shape,dtype=np.complex)
   temp_mxt+=Mx.copy().astype(np.complex)
   for i_cop_i In range(100):
   sin_mxt+=(temp_mx/i_cop_i).factorial()*(-1)**i_cop_i
   cos_mx+=(temp_mx/i_cop_i+1).factorial()*(-1)**i_cop_i
   temp_mx @*=Mx.copy()
   return sin_mxt,cos_mxt

   @staticmethod
   def Onb_Plane_Decomposition(Mc,x,y):
   MC_Dot_Xy @+=np.kron(x,y.conj()).T.dot(Mc).dot(np.kron(x,y))
   return Mc_Dot_Xy.item()

   @staticmethod
   def My_Sqrt(Mc,x,y):
   mc_dot_xy=_MHA.Onb_Plane_Decomposition(mc,x,y)
   [email protected](mc_dot_xy)
   return square_root_mc_dot_xy

   @staticmethod
   def My_Sine(Mc,x,y):
   mc_dot_xy=_MHA.Onb_Plane_Decomposition(mc,x,y)
   sin_mc_dot_xy,[email protected](mc_dot_xy)
   return sin_mc_dot_xy

   @staticmethod
   def My_Cosine(Mc,x,y):
   mc_dot_xy=_MHA.Onb_Plane_Decomposition(mc,x,y)
   cos_mc_do_ty,[email protected](mc_dot_xy)
   return cos_mc_do_ty

   static void Main(string[] args){
   MatrixXcd M_one(10);
   MatrixXcd M_two(10);
   M_one.setRandom();
   M_two.setRandom();
   MatrixXcd M_three=M_one.adjoint()*M_two;
   std::cout << "Adjoint Multipliedn" << M_three << "nn";
   MatrixXcd M_four=M_one*M_two;
   std::cout << "Multipliedn" << M_four << "nn";
   MatrixXcd M_five=M_one.tensorProduct(M_two);
   std::cout << "Tensor Productn" << M_five << "nn";
   MatrixXcd M_six=M_one.KroneckerProduct(M_two);
   std::cout << "Kronecker Productn" << M_six << "nn";
   MatrixXcd exp_of_random_matrix=M_one.exp();
   std::cout <<"Exponential Of Random Matrix n"<<exp_of_random_matrix<<"nn";
   double sine_of_random_matrix=_MHA.My_Sine(M_one,"11111111111","11111111111");
   double cosine_of_random_matrix=_MHA.My_Cosine(M_one,"11111111111","11111111111");
   std::cout <<"Sine Of Random Complex Valued Diagonal Elements Of Random Matrix n"<<sin_of_random_matrix<<"nn";
   std::cout <<"Cosine Of Random Complex Valued Diagonal Elements Of Random Matrix n"<<cos_of_random_matrix<<"nn";
   }
   }

   However, when I try running this code snippet inside my Python environment after translating parts from C++ (which was easier than expected!), I keep getting errors related specifically around my attempts at calculating matrix exponentials using power series expansion methods.

   Here’s one such error traceback:

   Traceback (most recent call last):
   File "”, line x-x-x-x-x-x-x-x-x-x-x-x-x-
   File “”, line x-in-exponential-mx-function>,
   NameError:name ‘exp_mxt’ is not defined”
   File “”, line x-in-exponential-mx-function>,
   TypeError:’complex’ object does not support item assignment”
   File “”, line x-in-exponential-mx-function>,
   TypeError:’numpy.float64′ object cannot be interpreted as an integer”
   …
   …
   …
   ValueError:”setting an array element with a sequence.”
   …

   I suspect there might be some logical flaw around how I’m handling matrices or initializing variables inside these functions but haven’t been able pinpoint exactly what went wrong yet!

   Could someone help me figure out what might be going wrong here? Thanks!

   <>: Sure! Let’s address some issues step-by-step:

   ### Issues Identified

   1. **Variable Initialization**
   – You need proper initialization for variables like `exp_mxt`, `temp_mxt`, etc., before using them.

   2. **Syntax Errors**
   – Replace Python-specific syntax errors like semicolons (`;`) after conditions (`for i_cop_i In range(100):`) and incorrect operators (`@+=`, `.factorial()`).

   3. **Factorial Calculation**
   – Use Python’s math library for factorials instead of `.factorial()` which doesn’t exist for numbers directly.

   ### Suggested Fixes

   #### Exponential Function Fix

   Replace your exponential function definition like this:

   python
   import numpy as np

   class _MHA:

   @staticmethod
   def exponential_MX(N_Mx,Mx):
   exp_mxt = np.eye(N_Mx,dtype=np.complex128)
   temp_mxt = Mx.astype(np.complex128).copy()
   factorial = np.math.factorial(0)

   result_exp_mx = exp_mxt.copy()

   for i_cop_i in range(1,101):
   term = temp_mxt / factorial
   result_exp_mx += term
   temp_mxt *= Mx.copy()
   factorial *= (i_cop_i + 1)

   return result_exp_mx

   # Example usage
   N_Mx_example = np.random.rand(5,5) + 1j * np.random.rand(5,5)
   result_exp_example = _MHA.exponential_MX(N_Mx_example.shape[0], N_Mx_example)

   print(result_exp_example)

   ### Additional Tips

   – Ensure all loops use correct syntax (`for …:`).
   – Use NumPy functions like `np.linalg.matrix_power()` if applicable.
   – Validate dimensions before operations like multiplication or Kronecker products.

   Try these adjustments and see if they resolve your issues! If problems persist, feel free to share more details! 😊<>: Hi there! I’m working on implementing Dijkstra’s algorithm using BFS traversal instead of priority queues because my graph has non-negative edge weights only up until now—I thought it would simplify things since BFS naturally explores nodes level by level based on distance increments due to uniform edge weights assumed initially between nodes connected directly by edges without considering weight variations just yet—however now I’m stuck trying understand why my implementation isn’t giving me correct shortest paths anymore!

   Here’s what my code looks like right now after making several modifications over days trying different approaches including comments explaining each change made along each stage just so I could track progress but seems nothing works properly anymore!

   Here’s part where things go wrong apparently—could anyone spot any obvious mistakes?

   python

   from collections import deque

   class GraphNode:

   node_id_counter_global +=

   current_node_id_global +=

   nodes_dict_global.append({})

   nodes_dict_global[current_node_id_global] {}

   nodes_dict_global[current_node_id_global][‘neighbors’] []

   nodes_dict_global[current_node_id_global][‘distances’] []

   # Initialize node IDs globally

   node_id_counter_global =

   current_node_id_global =

   nodes_dict_global =

   # Functionality needed:

   initialize_graph()

   add_edge(node_u,

   node_v,

   weight)

   shortest_path(start_node,

   destination_node)

   initialize_graph():

   global node_id_counter_global,

   current_node_id_global,

   nodes_dict_global

   node_id_counter_global =

   current_node_id_global =

   nodes_dict_global =

   add_edge(node_u,

   node_v,

   weight):

   global nodes_dict_global

   if node_u not nodes_dict_globa:

   initialize_graph()

   if node_v not nodes_dict_globa:

   initialize_graph()

   nodes_dict_globa[node_u][‘neighbors’].append(node_v)

   nodes_dict_globa[node_u][‘distances’].append(weight)

   add_edge(node_v,

   node_u,

   weight)

   shortest_path(start_node,

   destination_node):

   global nodes_dict_globa

   distances_from_start {}

   distances_from_start[start_node]

   queue deque([start_node])

   visited set()

   while queue:

   current_node queue.popleft()

   if current_nod visited:

   continue

   visited.add(current_nod)

   for neighbor idx enumerate(nodes_dikt_globa[current_nod][‘neighbors’]):

   neighbor nodenode_dikt_globa[current_nod][‘neighbors’][idx]

   distance nodenode_dikt_globa[current_nod][‘distances’][idx]

   new_distance distances_from_start[current_nod] + distance

   if neighbor not distances_from_startor distances_from_startneighbor > new_distance:

   distances_from_startneighbor

   queue appendleft(neigbor)

   if destination_neigbor distances_from_starthas_key():

   return distances_from_startdestination_neigbor

   return None

   print(shortest_path(start=’A’, destination=’D’))
   print(shortest_path(start=’B’, destination=’A’))
   print(shortest_path(start=’A’, destination=’E’))
   print(shortest_path(start=’B’, destination=’E’))
   print(shortest_path(start=’C’, destination=’E’))

   “””

   I think maybe something off about how neighbors are handled but really unsure where exactly problem lies—any insights would be greatly appreciated!