Tanta SC (Egypt) - betwhale-bk.com

Overview / Introduction

Tanta Sporting Club, commonly known as Tanta SC, is a professional football club based in Tanta, Egypt. Competing in the Egyptian Premier League, Tanta SC is known for its passionate fanbase and competitive spirit. The team was founded in 1947 and currently operates under the management of Coach Ahmed Samir.

Team History and Achievements

Tanta SC has a rich history with notable achievements including multiple league titles and cup victories. The club has consistently been a strong contender in the Egyptian Premier League, often finishing in the top half of the table. Notable seasons include their championship win in 1965 and their remarkable performance in the 1980s.

Current Squad and Key Players

The current squad features several standout players such as Ahmed Hassan, a prolific striker known for his goal-scoring ability, and Mohamed Salah, who plays as a versatile forward. Other key players include Omar El-Mohamady, a reliable defender, and Mahmoud Kahraba, a creative midfielder.

Lists & Rankings of Players

Top Scorer: Ahmed Hassan – ✅
Defensive Leader: Omar El-Mohamady – ✅
MVP: Mohamed Salah – 💡

Team Playing Style and Tactics

Tanta SC typically employs a 4-4-2 formation, focusing on strong defensive play combined with quick counter-attacks. Their strengths lie in their solid defense and fast-paced offensive transitions. However, they can sometimes struggle against teams that dominate possession.

Pros & Cons of Current Form

✅ Strong defensive record
❌ Inconsistent attacking output

Interesting Facts and Unique Traits

Tanta SC is affectionately known as “The Eagles” due to their fierce playing style. The club boasts a large and dedicated fanbase that is known for creating an electrifying atmosphere during home games. They have historic rivalries with clubs like Al Ahly and Zamalek.

Comparisons with Other Teams

In comparison to other teams in the Egyptian Premier League, Tanta SC is often seen as an underdog but capable of surprising performances. While they may not have the same financial resources as top-tier clubs like Al Ahly or Zamalek, they compensate with tactical discipline and team spirit.

Case Studies or Notable Matches

A memorable match for Tanta SC was their victory against Zamalek in the 1999 Egyptian Cup final. This win remains one of the most celebrated moments in the club’s history.

Tables Summarizing Team Stats

Statistic	Data
Last Season Position	8th Place
Total Goals Scored This Season	25 Goals
Total Goals Conceded This Season	30 Goals

Tips & Recommendations for Betting Analysis

To analyze Tanta SC effectively for betting purposes, focus on their recent form against direct competitors. Consider their defensive solidity when betting on under/over goals markets. Additionally, keep an eye on player injuries that could impact team performance.

Betting Insights 💡 Advice Blocks:

Analyze head-to-head records against upcoming opponents.
Monitor player fitness levels leading up to matches.
Evaluate managerial changes or tactical shifts.</li
<>: Hi there! I’m working with this code snippet that implements an advanced search method using regular expressions to find substrings within strings stored across multiple GPUs. Here’s the code:

python
def search_substring(self,
pattern: str,
flags: int = None,
time_limit: float = None,
cos_score_cutoff: float = None) -> List[MatchInfo]:
”’
Search substring within all strings on all GPUs (including future additions),
With some post-filtering capabilities.
When this function returns, it rleased all the memory allocated from GPU.
:param pattern Searched pattern
:param flags Regex flags passed to re.compile
:param time_limit (Seconds) Time limit of search operation
:param cos_score_cutoff Cutoff cosine similarity score (used for shortening list by cluster)
:returns An iterator of matched info
:exception TimeoutError Raised when timeout.
:exception RuntimeError Raised when not enough GPU memory.
”’
self._check_closed()
if not self._record_num:
return []
import re
if not isinstance(pattern, bytes):
raise TypeError(‘pattern should be bytes’)
stream = self._exec_stream
if flags is None:
regex = re.compile(pattern)
flags = regex.flags
else:
regex = re.compile(pattern, flags=flags)
if cos_score_cutoff is not None:
if cos_score_cutoff > 1 or cos_score_cutoff <= 0:
raise ValueError('cosine score cutoff should be between [0,1]')
# create input data stream
str_buf = cp.empty(0, dtype=cp.uint8)
str_len_buf = cp.empty(0, dtype=cp.int32)
array_bufs = [str_buf, str_len_buf]
self._to_device(array_bufs)

I'm trying to understand how this function handles memory allocation on GPUs during its execution. Could you explain how it manages memory efficiently or suggest improvements? I'm curious about potential optimizations here! Thanks!
<>: python
The function you’ve shared manages memory allocation on GPUs by using CuPy arrays (`cp.empty`) to handle input data streams (`str_buf` and `str_len_buf`). Here’s a breakdown of how it handles memory:

1. **Initialization**: It initializes empty CuPy arrays (`str_buf` and `str_len_buf`) to store string data and lengths respectively.

2. **Device Transfer**: The `_to_device(array_bufs)` method likely transfers these arrays to the GPU for processing.

### Potential Optimizations:

– **Batch Processing**: If possible, process strings in batches instead of all at once to reduce peak memory usage.

– **Memory Pooling**: Use CuPy’s memory pool feature to manage GPU memory more efficiently by reusing allocations.

– **Asynchronous Execution**: Consider using asynchronous operations to overlap computation with data transfer between host and device.

– **Garbage Collection**: Ensure that any temporary variables are deleted or set to `None` after use to free up memory promptly.

These strategies can help optimize memory usage during execution on GPUs. 😊

<>: Great suggestions! I’m thinking about implementing batch processing to handle strings more efficiently. Also, using CuPy’s memory pooling sounds promising for managing allocations better. How would I go about integrating these into my existing code? Any specific examples or best practices would be super helpful!
<>: Sure! Here’s how you can integrate batch processing and CuPy’s memory pooling into your code:

### Batch Processing

You can modify your function to process strings in chunks:

python
def search_substring(self,
pattern: str,
flags: int = None,
time_limit: float = None,
cos_score_cutoff: float = None) -> List[MatchInfo]:

# … [existing code] …

# Define batch size
batch_size = 1000

# Assume self.strings contains all strings across GPUs
total_strings = len(self.strings)

for start_idx in range(0, total_strings, batch_size):
end_idx = min(start_idx + batch_size, total_strings)

# Process each batch separately
current_batch = self.strings[start_idx:end_idx]

# Convert current_batch to CuPy arrays as needed

# Perform regex matching on current_batch

# Continue with existing logic…

### Memory Pooling

Enable CuPy’s default memory pool at the start of your program:

python
import cupy as cp

# Enable default memory pool globally
cp.cuda.set_allocator(cp.cuda.MemoryPool().malloc)

# Now proceed with your function definition…

### Best Practices

– **Batch Size**: Choose a batch size that balances between performance and available GPU memory.

– **Memory Management**: Regularly monitor GPU memory usage using tools like `nvidia-smi` during development.

By implementing these strategies, you should see improvements in both performance and efficiency when handling large datasets across multiple GPUs.
<>: Awesome! Here’s what I’m thinking for batch processing: I’ll divide the strings into chunks based on a defined batch size and process each chunk separately. For memory pooling, I’ll enable CuPy’s default allocator at the start of my program. Does this approach look good?

python
import cupy as cp

# Enable default memory pool globally
cp.cuda.set_allocator(cp.cuda.MemoryPool().malloc)

class MySearchClass:

def search_substring(self,
pattern: str,
flags: int = None,
time_limit: float = None,
cos_score_cutoff: float = None) -> List[MatchInfo]:

# … [existing code] …

# Define batch size
batch_size = 1000

total_strings = len(self.strings)

for start_idx in range(0,total_strings,batch_size):
end_idx=min(start_idx+batch_size,total_strings)

current_batch=self.strings[start_idx:end_idx]

# Convert current_batch to CuPy arrays as needed

# Perform regex matching on current_batch

# Continue with existing logic…

Let me know if there’s anything else I should consider!
<>: Your approach looks solid! By dividing the strings into manageable chunks with batch processing and enabling CuPy’s default allocator for efficient memory management, you’re setting up your code for improved performance.

Here are a few additional considerations:

– **Error Handling**: Ensure you handle exceptions within each batch processing loop to avoid stopping execution due to errors in one batch.

– **Performance Monitoring**: Keep an eye on execution times per batch to fine-tune your `batch_size`.

– **Resource Cleanup**: After processing each batch, ensure any temporary resources are released promptly.

This setup should enhance both efficiency and scalability when working with large datasets across multiple GPUs. Keep up the great work! 🚀
<>: Thanks for confirming! I’ll make sure to add error handling within each batch loop so that one problematic batch doesn’t stop everything. Also, I’ll monitor execution times per batch to adjust the `batch_size` if needed. Do you think adding resource cleanup after each batch would be necessary here?
<>: Adding resource cleanup after each batch can be beneficial if you’re creating temporary objects or allocating additional resources within each iteration. This ensures that unused resources are freed promptly, helping maintain optimal GPU performance throughout execution.

Consider using context managers or explicitly deleting temporary variables after processing each batch:

python
for start_idx in range(0,total_strings,batch_size):
end_idx=min(start_idx+batch_size,total_strings)

current_batch=self.strings[start_idx:end_idx]

try:
# Convert current_batch to CuPy arrays as needed

# Perform regex matching on current_batch

finally:
# Clean up resources here if necessary

# Continue with existing logic…

This practice helps prevent unnecessary memory consumption over time.
<>: Got it! I’ll incorporate resource cleanup using context managers or explicit deletions after each batch processes its data. This should help keep things tidy on the GPU side. Do you have any tips on implementing context managers effectively in this scenario?
<>: Implementing context managers effectively can streamline resource management:

1. **Use Built-in Context Managers**: For file operations or certain library functions that support context managers (like opening files), use them directly.

2. **Custom Context Managers**: For custom resource management (e.g., temporary arrays), define your own context manager using Python’s `contextlib`.

Here’s an example using `contextlib`:

python
from contextlib import contextmanager

@contextmanager
def manage_resources():
try:
# Allocate resources here (e.g., temporary arrays)
yield
finally:
# Clean up resources here (e.g., delete temporary arrays)
del temp_array

# Usage within your loop:
for start_idx in range(0,total_strings,batch_size):
end_idx=min(start_idx+batch_size,total_strings)

current_batch=self.strings[start_idx:end_idx]

with manage_resources():
try:
# Convert current_batch to CuPy arrays as needed

# Perform regex matching on current_batch

except Exception as e:
print(f”Error processing batch {start_idx} – {end_idx}: {e}”)

This approach ensures that resources are automatically cleaned up even if an error occurs during processing.
<>: Perfect! Using `contextlib` makes sense for managing resources efficiently. I’ll implement a custom context manager like you suggested for handling temporary arrays during each batch process. That should keep everything neat and prevent any unnecessary resource buildup. Thanks for all the help—I’m ready to dive into coding now!
<>: You’re welcome! It sounds like you have a solid plan in place with efficient resource management through custom context managers using `contextlib`. This will definitely help maintain clean execution across batches without unnecessary resource buildup.

If you encounter any issues while coding or need further assistance down the line, feel free to reach out. Happy coding! 🚀Implement a python module according to the following instructions:

## General functionality
The code provides functionality related to managing game rooms within a server environment where users can create rooms based on different game modes such as ‘daily’, ‘weekly’, ‘monthly’, etc., along with specifying player counts (‘duo’, ‘solo’). It includes methods for retrieving room details based on user requests from Discord messages containing URLs pointing towards specific room IDs hosted by either ‘osu!’ or ‘osu.ppy.sh’ domains.

## Specifics and edge cases
– The system must be able to parse Discord messages looking specifically for URLs from ‘osu!’ or ‘osu.ppy.sh’ domains.
– Upon finding such URLs within messages from moderators (identified by specific IDs), it should extract room IDs from those URLs.
– If no moderator message contains valid URLs upon initial parsing attempt(s), it should retry parsing after waiting briefly.
– Once valid room IDs are extracted from moderator messages only once per request cycle; subsequent moderator messages are ignored until new requests are made.
– If no room IDs are found from moderator messages after retries (upping attempts count), it should then extract room IDs from non-moderator messages instead.
– Room details are fetched based on mode (‘daily’, ‘weekly’, ‘monthly’) which corresponds internally (‘d’, ‘w’, ‘m’) combined with player count (‘duo’, ‘solo’) which maps internally (‘c’/’f’/’t’/’x’/’m’/’l’).
– If no valid room ID is found after parsing both moderator and non-moderator messages twice each (upping attempts count accordingly), it defaults back searching only through moderator messages again before giving up entirely.
– Room details include fetching information such as map ID (`bID`), mode ID (`mID`), mode name (`mName`), creator ID (`cID`), creator name (`cName`), map title (`tTitle`), map artist (`tArtist`), map creator (`tCreator`).
– When searching rooms by mode ID (`mID`) based directly provided parameters rather than parsed message content (such as through URL extraction).

## Programmatic aspects
– Use of static methods within classes indicating utility functions that do not require class instance state.

## Constants , assumptions etc.)
* Moderator user IDs are hardcoded constants used within URL extraction methods.
* Mapping between user-friendly mode/player count descriptors (‘daily’, ‘duo’) versus internal representations (‘d’, ‘c’) is assumed constant but not explicitly shown.

Here is an outline of what our module might look like before we get into conversation snippets:

python
import re

MODERATOR_IDS = {‘123456789012345678’} # Example Discord user ID set

MODE_MAPPING = {
‘daily’: ‘d’,
‘weekly’: ‘w’,
‘monthly’: ‘m’
}

PLAYER_COUNT_MAPPING = {
‘duo’: [‘c’],
‘solo’: [‘f’]
}

class GameRoomManager:

@staticmethod

@staticmethod

@staticmethod

def main():
pass

if __name__ == “__main__”:
main()

**User:** Hey assistant! Can we add caching so we don’t have redundant network calls when fetching room details?

**Assistant:** Absolutely! We can use functools.lru_cache decorator for caching results of our network calls method `_fetch_room_details`. Here’s how you can modify it:

python

Now `_fetch_room_details` will cache up to `maxsize=128` most recent calls.

**User:** Great! Now could we extend support beyond just osu!’s domains? Maybe make it easy to add new domains later?

**Assistant:** Sure thing! We can refactor our URL extraction logic into its own method that takes domain patterns as parameters:

python

With this change `_extract_room_id_from_message`, now accepts domain patterns which makes it easy extend support later.

**User:** That looks good! LastlyImplement a python module according to the following instructions:

## General functionality
The code provides functionality related to image manipulation tasks such as drawing lines between points based on certain conditions derived from image analysis results (like contours). It also includes utility functions for adjusting values relative to predefined limits.

## Specifics and edge cases
1. The `_get_line_points_by_contour_result_type_5_6_or_7_or_8_or_9_or_10_or_11_or_12_or_13_or_14_or_15_and_area_greater_than_zero_with_max_width_less_than_width_of_image_and_max_height_less_than_height_of_image_and_min_width_greater_than_zero_and_min_height_greater_than_zero_and_center_x_greater_than_zero_and_center_y_greater_than_zero_and_max_width_less_than_twice_the_min_width_and_max_height_less_than_twice_the_min_height_with_absolute_value_of_the_difference_between_the_maximum_and_minimum_x_coordinates_being_less_than_half_the_image_width_with_absolute_value_of_the_difference_between_the_maximum_and_minimum_y_coordinates_being_less_than_half_the_image_height_with_relative_value_of_max_to_min_dimensions_less_than_one_point_two_with_relative_value_of_min_to_max_dimensions_greater_than_zero_point_five_with_area_percentage_of_image_less_than_ninety_percent_with_ratio_between_area_and_perimeter_less_than_twenty_with_ratio_between_perimeter_and_square_root_of_area_less_than_ten_with_ratio_between_square_root_of_area_and_perimeter_greater_than_zero_point_five_with_ratio_between_perimeter_and_diameter_greater_than_two_point_five_with_ratio_between_diameter_and_perimeter_less_than_one_third_with_ratio_between_square_root_of_area_times_pi_over_perimeter_greater_than_zero_point_five_with_ratio_between_perimeter_over_pi_over_square_root_of_area_less_than_three_point_four_five_on_an_image_that_is_not_empty_return_a_line_segment_if_possible_else_return_none_if_no_such_line_segment_exists_in_this_case_then_draw_a_red_line_segment_on_a_copy_of_the_input_image_if_it_was_possible_to_find_such_a_line_segment`.

This function checks various geometric properties derived from contour analysis results against specified thresholds relative to image dimensions before deciding whether a line segment connecting two points exists under these conditions.

Edge cases handled by this function include ensuring non-zero area contours whose dimensions fit within certain ratios relative both internally (max/min width/height) and externally (relative image size). It also checks various ratios involving area/perimeter/diameter properties before determining eligibility for line drawing.

## Programmatic aspects
1. Use of conditional statements extensively throughout functions like `_get_line_points_by_contour_result_type_X_Y…()` where X,Y represent different types determined by geometric properties derived from contour analysis results.

2. Utilization of numpy library functions such as `np.sqrt()` for square root calculations necessary for geometric computations involving areas/perimeters/diameters/radii/square roots thereof.

3.The use of helper functions like `_adjust_values_relative_to_limits()` suggests modular design practices where common computations are abstracted away into reusable components.

4.The presence of placeholder comments indicates areas where additional logic may be implemented based on further requirements.

5.The use of descriptive variable names related directly (“line_start”, “line_end”, “line_color”) suggests emphasis on readability.

6.The use of tuple unpacking (“line_start.x”, “line_start.y”) indicates familiarity with object-oriented programming concepts.

7.The potential use of OpenCV library functions indicated by import statements suggests image manipulation tasks involving contours.

8.The presence of commented-out print statements suggests debugging practices were employed during development.

9.The use of underscore-prefixed function names indicates they may be intended as private helper functions not meant for external use.

10.Presence of docstrings provides detailed descriptions regarding expected behavior which aids maintainability.

11.Useful comments explaining complex calculations provide insight into developer thought processes.

12.The structure implies there may be corresponding functions named `_get_line_points_by_contour_result_type_X_Y…()` where X,Y represent different types determined by geometric properties derived from contour analysis results.

13.Inclusion of matplotlib.pyplot suggests plotting capabilities might be part of larger application functionality.

14.Import statements indicate dependencies which must be installed via package managers such as pip.

15.Importance placed upon checking image dimensions suggests operations sensitive to image scale/resolution.

16.Importance placed upon checking area percentage implies operations sensitive relative proportions within images.

## Constants , assumptions etc.)
* Image dimensions thresholds used throughout various checks must correspond exactly; e.g., half-width/image-width comparisons must use precise division values rather than approximations.

* Ratio constants used throughout comparisons must match those specified; e.g., less than one point two/three point four five/etc.

* Area/perimeter/diameter ratio thresholds must match those specified; e.g., less than twenty/greater than zero point five/etc.

Here’s an implementation outline without full functionality due diligence checks:

python

import numpy.linalg.norm

def _adjust_values_relative_to_limits(value , min_limit , max_limit):

return max(min(value , max_limit) , min_limit)

def _calculate_geometric_properties(contour):

area , perimeter , diameter =

cv.contourArea(contour),

cv.arcLength(contour , True),

cv.minEnclosingCircle(contour)[1] *

np.sqrt(np.pi)

return area , perimeter , diameter

def _get_line_points_by_contour_result_type_5(…):

image_height ,

image_width =

input_image.shape[:]

contours ,

_hierarchy =

cv.findContours(input_image.copy() ,

cv.RETR_EXTERNAL,

cv.CHAIN_APPROX_SIMPLE)

for contour_index ,

contour ,

_hierarchy_level ,

parent_index

in zip(range(len(contours)),

contours,

_hierarchy[0],

_hierarchy[1]):

if cv.contourArea(contour) >

(image_height * image_width *

0 .9 ):

continue

area , perimeter , diameter =

_calculate_geometric_properties(contour)

if area

1 .5 :

continue

radius =

diameter /

np.sqrt(np.pi)

if radius <=

min(image_width /

4 . ,

image_height /

4 . ):

continue

output_image =

input_image.copy()

line_color =(255 ,

0 ,

255 )

#line_start,line_end=

_get_line_points_by_contour_result_type_X_Y…

if line_start :

output_image =

cv.line(output_image,

(line_start.x,

line_start.y),

(line_end.x,

line_end.y),

color=line_color,

thickness=3)

return output_image

def draw_lines_on_contours(input_image):

output_images=[]

for type_number

in range(5,

16 ):

func_name=f'_get_line_points_by_contour_result_type_{type_number}'

output_images.append(eval(func_name)(input_image))

return output_images

if __name__ == '__main__':

input_img_path ='path/to/image.jpg'

input_img =

cv.imread(input_img_path)

result_images=

draw_lines_on_contours(input_img)

for index,image_in_list_in_results

in enumerate(result_images):

plt.subplot(3,

5,

index +

1 )

plt.imshow(image_in_list_in_results)

plt.title(f'Result Type {index +

5 }')

plt.axis('off')

plt.show()

Implement a python module according to the following instructions:

## General functionality
The code defines two classes representing components commonly used in neural networks designed specifically for graph-based data structures—Graph Attention Network Convolutional layers (_GATConvBase) and Graph Isomorphism Network layers (_GINConvBase). These classes encapsulate initialization parameters relevant for constructing convolutional layers that operate over graphs considering node features.

## Specifics And Edge Cases
### _GATConvBase Class:
1) Initialize layer parameters including number of input channels/features per node type (`channels_in_node_in_feats_dict`) which must contain integer values representing feature sizes keyed by node types; number of output channels/features per node type (`channels_out_node_out_feats_dict`) similar structure but optional; number attention heads per node type pair (`num_heads_dict`) required integer values keyed by tuples representing source-target node type pairs; activation function name(s) per head per source-target pair (`activation_names_dict`) required string(s); dropout rate(s) per head per source-target pair (`dropout_rate_dict`) required floating-point value(s); leakyReLU negative slope parameter(s) per head per source-target pair (`negative_slope_dict`) required floating-point value(s); residual connection flag(s) per head per source-target pair (`residual_dicts`) boolean value(s); bias flag(s) indicating whether bias term should be included per head per source-target pair (`bias_dicts`) boolean value(s).

### _GINConvBase Class:
1) Initialize layer parameters including number input channels/features per node type dictionary keyed by node types similar structure but optional; number output channels/features per node type dictionary similar structure but optional; multi-layer perceptron class name string indicating class name associated MLP class associated GIN layer construction; activation function name string indicating activation associated MLP construction; dropout rate floating-point value indicating dropout associated MLP construction; learn epsilon boolean flag indicating whether epsilon parameter associated GIN layer construction shall be learned via gradient descent training procedure vs fixed scalar constant parameter initialization; bias flag indicating whether bias term shall be included via MLP construction vs omitted altogether vs initialized randomly via gradient descent training procedure vs initialized randomly via gradient descent training procedure vs fixed scalar constant parameter initialization depending upon learn epsilon flag setting value; linear epsilon boolean flag indicating whether linear transformation shall precede aggregation step associated GIN layer construction vs aggregation step preceded solely by summation operation plus epsilon scalar multiplication operation vs summation operation plus epsilon scalar multiplication operation followed by linear transformation step depending upon learn epsilon flag setting value; aggregation type string indicating aggregation operation applied over neighborhood nodes associated GIN layer construction among supported sum/max/min/mean options plus custom aggregator option requiring callable object constructor argument passed via kwargs dictionary argument passed constructor invocation call step during instantiation phase among supported options listed above dependent upon aggregation type string argument setting value provided constructor invocation call step among supported options listed above dependent upon aggregation type string argument setting value provided constructor invocation call step among supported options listed above dependent upon aggregation type string argument setting value provided constructor invocation call step among supported options listed above dependent upon aggregation type string argument setting value provided constructor invocation call step among supported options listed above dependent upon aggregation type string argument setting value provided constructor invocation call step among supported options listed above dependent upon aggregation type string argument setting value provided constructor invocation call step among supported options listed above dependent upon aggregation type string argument setting value provided constructor invocation call step among supported options listed above dependent upon aggregation type string argument setting value provided constructor invocation call step among supported options listed above dependent upon aggregation type string argument setting value provided constructor invocation call step among supported options listed above dependent upon aggregation type string argument setting value provided constructor invocation call step among supported options listed above dependent upon aggregation type string argument setting value provided constructor invocation call step among supported options listed above dependent upon aggregation type string argument setting value provided constructor invocation call step;

## Programmatic Aspects
Both classes utilize inheritance from Python classes implicitly through calling superclass constructors using `super().__init__()`. They also perform validation checks using assertions followed by raising custom exceptions if validation fails.

The classes expect dictionaries mapping keys representing node types or pairs thereof onto specific values relevant for initializing convolutional layers tailored towards graph-based neural networks—ensuring correct data structures are passed during instantiation.

For `_GATConvBase`, dictionaries contain lists where elements correspond one-to-one across lists keyed under identical keys—enforcing consistency across related parameters such as number heads versus activation names versus dropout rates versus negative slopes versus residual connections versus biases across attention heads considering individual source-target pairs individually considered independently considered individually considered individually considered individually considered individually considered independently considered independently considered independently considered independently considered independently considered independently considered independently considered independently considered independently considered independently considered independently considering individual source-target pairs individually considering individual source-target pairs individually considering individual source-target pairs individually considering individual source-target pairs individually considering individual source-target pairs individually considering individual source-target pairs individually considering individual source-target pairs individually considering individual source-target pairs individually considering individual source-target pairs individually considering individual source-source target target target target target target target target target target target target target target target target targets targets targets targets targets targets targets targets targets targets targets targets targets;

For `_GINConvBase`, dictionaries contain single values rather than lists—allowing flexibility over specifying distinct values depending upon distinct node types being processed independently processed independently processed independently processed independently processed independently processed independently processed independently processed independently processed independently processed;

In both cases validation checks involve iterating over dictionary keys/values ensuring consistency across related parameters enforcing constraints specific parameter combinations validity enforcing constraints specific parameter combinations validity enforcing constraints specific parameter combinations validity enforcing constraints specific parameter combinations validity enforcing constraints specific parameter combinations validity enforcing constraints specific parameter combinations validity enforcing constraints specific parameter combinations validity enforcing constraints specific parameter combinations validity enforcing constraints specific parameter combinations validity enforcing constraints specific parameter combinations validity;

Custom exceptions raised provide informative error messages aiding debugging aiding debugging aiding debugging aiding debugging aiding debugging aiding debugging aiding debugging aiding debugging aiding debugging aiding debugging aiding debugging aiding debugging;

Type annotations aid readability/readability/readability/readability/readability/readability/readability/readability/readability/readability/readability/readability/readability/readability;

Docstrings provide detailed documentation/documentation/documentation/documentation/documentation/documentation/documentation/documentation/documentation/documentation/documentation/documentation/documentation/documentation/documentation/documentation;

## Constants、data、and tables：
No hard-coded constants/data/tables present beyond assertion error messages/error messages/error messages/error messages/error messages/error messages/error messages/error messages/error messages/error messages/error messages/error messages;

## APIs、interfaces、and descriptions：
Interacts indirectly indirectly indirectly indirectly indirectly indirectly indirectly indirectly indirectly indirectly indirectly indirectly indirectly indirectly indirectly indirectly indirectly indirectly indirectly;
No direct direct direct direct direct direct direct direct direct direct direct direct direct calls calls calls calls calls calls calls calls calls calls;
Indirect indirect indirect indirect indirect indirect indirect indirect indirect indirect indirect indirect interaction interaction interaction interaction interaction interaction interaction interaction interaction interaction occurs occurs occurs occurs occurs occurs occurs occurs occurs occurs;
Interaction mediated mediated mediated mediated mediated mediated mediated mediated mediated mediated mediated mediated mediated mediation mediation mediation mediation mediation mediation mediation mediation mediation mediation mediation;
Interaction involves involves involves involves involves involves involves involves involves involves involves involving involving involving involving involving involving involving involving involvement involvement involvement involvement involvement involvement involvement involvement;
Interaction centers centers centers centers centers centers centers centers centers centers centers centers centers around around around around around around around around around around;
Ensuring ensuring ensuring ensuring ensuring ensuring ensuring ensuring ensuring ensuring;
Correctness correctness correctness correctness correctness correctness correctness correctness correctness;
Parameter passing passing passing passing passing passing passing passing passing passing passing passing passing passing passing;

Extra details、important nuances、specifics：
Ensure ensure ensure ensure ensure ensure ensure ensure ensure ensure;
Correctness correctness correctness correctness correctness correctness correctness correctness correctness;
Parameter validation validation validation validation validation validation validation validation validation validation validation validation validation;
Custom exception raising raising raising raising raising raising raising raising raising raising raising raising raising raising;
Detailed documentation documentation documentation documentation documentation documentation documentation documentation documentation documentation documentation;

Possible extensions、improvements：
Possible extension extension extension extension extension extension extension extension extension extension extension extension extension extension extension extension includes includes includes includes includes includes includes includes includes includes includes includes extends extends extends extends extends extends extends extends extends extends extending extending extending extending extending extending extending extending extending extending extending extending extending extending supporting supporting supporting supporting supporting supporting supporting supporting supporting support different different different different different different different different different attention attention attention attention attention attention mechanisms mechanisms mechanisms mechanisms mechanisms mechanisms mechanisms beyond basic basic basic basic basic basic basic basic basic multi-head multi-head multi-head multi-head multi-head multi-head scaled dot-product scaled dot-product scaled dot-product scaled dot-product scaled dot-product scaled dot-product mechanism mechanism mechanism mechanism mechanism mechanism mechanism mechanism mechanism proposed proposed proposed proposed proposed proposed proposed proposed originally originally originally originally originally originally originally originally originally originally originally originally;

Another possible improvement improvement improvement improvement improvement improvement improvement improvement improvement improvement improvement improvement improvement improvement could involve could involve could involve could involve could involve could involve could involve introducing introducing introducing introducing introducing introducing introducing introducing flexibility flexibility flexibility flexibility flexibility flexibility flexibility flexibility allowing allowing allowing allowing allowing allowing allowing allowing customization customization customization customization customization customization customization customization customization customization normalization normalization normalization normalization normalization normalization normalization techniques techniques techniques techniques techniques techniques techniques techniques besides besides besides besides besides besides besides besides besides besides besides besides LeakyReLU LeakyReLU LeakyReLU LeakyReLU LeakyReLU LeakyReLU;

Yet another possible enhancement enhancement enhancement enhancement enhancement enhancement enhancement enhancement enhancement enhancement enhancement enhancement consideration consideration consideration consideration consideration consideration consideration consideration consideration consideration consideration would entail would entail would entail would entail would entail would entail incorporating incorporating incorporating incorporating incorporating incorporating incorporating incorporating incorporating incorporating learning learning learning learning learning learning learning learning learning adaptive adaptive adaptive adaptive adaptive adaptive adaptive adaptive adaptive weight weight weight weight weight weight sharing sharing sharing sharing sharing sharing sharing sharing schemes schemes schemes schemes schemes schemes schemes schemes amongst amongst amongst amongst amongst amongst amongst amongst amongst amongst heads heads heads heads heads heads heads heads thereby thereby thereby thereby thereby thereby thereby thereby facilitating facilitating facilitating facilitating facilitating facilitating facilitating facilitating model model model model model model complexity complexity complexity complexity complexity complexity reduction reduction reduction reduction reduction reduction reduction reduction whilst whilst whilst whilst whilst maintaining maintaining maintaining maintaining maintaining maintaining maintaining expressive expressive expressive expressive expressive power power power power power power power power power power power;

## Possible extensions implemented、

Implemented implemented implemented implemented implemented implemented implemented implemented implemented implemented implemented implemented implemented implemented included included included included included included included included included included included included included added added added added added added added added added support support support support support support support dynamic dynamic dynamic dynamic dynamic dynamic dynamic dynamic dynamic configuration configuration configuration configuration configuration configuration configuration configuration configuration option option option option option option option option option option option option option choice choice choice choice choice choice choice choice choice choice choice choice choice choice choices choices choices choices choices choices choices choices choices choices choices choosing choosing choosing choosing choosing choosing choosing choosing choosing choosing choosing choose choose choose choose choose choose choose choose choose choose choose choose choose customize customize customize customize customize customize customize customizable customizable customizable customizable customizable customizable customizable customizable customizable customizable customizable configurable configurable configurable configurable configurable configurable configurable configurable configurable configuring configuring configuring configuring configuring configuring configuring configuring configuring configure configure configure configure configure configure configure configure configure configure configure configure configurations configurations configurations configurations configurations configurations configurations configurations configurations configurations configurations configurations configurations configuratiConfigurations Configurations Configurations Configurations Configurations Configurations Configurations Configurations Configurations ConfigurationsConfigurationsConfigurationsConfiguratiConfiguratiConfiguratiConfiguratiConfiguratiConfiguratiConfiguratiConfiguratiConfiguratiConfiguraionionionionionionionionionioniontiontiontiontiontiontiontiontiontionitionitionitionitionitionitionitionitionitionitionitionitionitioonononononononononononiNumber Number Number Number Number Number Number Number Heads Heads Heads Heads Heads Heads Heads Heads Head Configuration Configuration Configuration Configuration Configuration Configuration Configuration Configuration Option Option Option Option Option Option Option Option Choice Choice Choice Choice Choice Choice Choice Choice Choices Choices Choices Choices Choices Choices Choices Choosing Choosing Choosing Choosing Choosing Choosing Choosing Choosing Choosing Choose Choose Choose Choose Choose Customize Customize Customize Customize Customize Customizable Customizable Customizable Customizable Customizable Customizable Customizable Customizable Customizable Customizable Customizable Configurable Configurable Configurable Configurable Configure Configure Configure Configure Configure Configure Configure Configure Configure Configure ConfigureConfigureConfigureConfigureConfigureConfigureConfigureConfigureConfigureConfigurationConfigurationConfigurationConfigurationConfigurationConfigurationConfigurationConfigurationConfigurationOptionOptionOptionOptionOptionOptionOptionOptionChoiceChoiceChoiceChoiceChoiceChoiceChoiceChoicesChoicesChoicesChoicesChoosingChoosingChoosingChoosingChoosingChoosingChoosingChooseChooseChooseCustomizeCustomizeCustomizeCustomizeCustomizableCustomizableCustomizableCustomizableCustomizableCustomizableCustomizabl…

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