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Bayern Munich W: Champions in Bundesliga Excellence – Squad, Stats, Achievements

Overview / Introduction about the Team

Bayern Munich Women, often referred to as FC Bayern Munich (w), is a premier football club based in Munich, Germany. Competing in the Frauen-Bundesliga, the top tier of women’s football in Germany, the team is known for its dynamic play and strong performances. Founded in 1970, Bayern Munich (w) has grown to become one of the most successful teams in women’s football under the management of Thomas Wörle.

Team History and Achievements

Bayern Munich (w) boasts a rich history filled with numerous titles and accolades. They have won multiple Frauen-Bundesliga championships and have been regular contenders for domestic and European honors. Notable achievements include their UEFA Women’s Champions League victories and consistent top finishes in the Bundesliga.

Current Squad and Key Players

The current squad features a mix of seasoned veterans and promising talents. Key players include:

  • Lena Goeßling – Midfielder
  • Nicole Billa – Forward
  • Katharina Baunach – Defender

Team Playing Style and Tactics

Bayern Munich (w) typically employs a 4-3-3 formation, focusing on possession-based play with quick transitions. Their strengths lie in their attacking prowess and solid defense, though they occasionally struggle with maintaining consistency throughout matches.

Interesting Facts and Unique Traits

The team is affectionately known as “Die Roten” due to their iconic red jerseys. They have a passionate fanbase and fierce rivalries, particularly with VfL Wolfsburg. Traditions include pre-match rituals that engage both players and supporters.

Lists & Rankings of Players, Stats, or Performance Metrics

  • ✅ Top Scorer: Nicole Billa – 15 goals this season
  • ❌ Lowest Performer: TBD based on recent form analysis
  • 🎰 Most Assists: Lina Magull – 10 assists this season
  • 💡 Best Defensive Player: Katharina Baunach – 5 clean sheets this season

Comparisons with Other Teams in the League or Division

Bayern Munich (w) often compares favorably against other top teams like VfL Wolfsburg due to their balanced squad depth and tactical flexibility. While Wolfsburg may have more international stars, Bayern’s home advantage often tilts matches in their favor.

Case Studies or Notable Matches

A standout match was their UEFA Women’s Champions League final victory last season against Paris Saint-Germain. This game showcased Bayern’s tactical acumen and resilience under pressure.

</tr
<>: Hi there! I’m working on a function that checks if two strings are at most one edit away from each other. Here’s what I have so far:

python
def one_away(s1, s2):
# Quick check for length difference
if abs(len(s1) – len(s2)) > 1:
return False
# If they’re the same length…
if len(s1) == len(s2):
found_difference = False
for i in range(len(s1)):
if s1[i] != s2[i]:
if found_difference:
return False
found_difference = True
return True
else:
# Assume without loss of generality that s1 is shorter than s2
if len(s1) > len(s2):
s1,s2 = s2,s1
for i in range(len(s1)):
if s1[i] != s2[i]:
return s1[i:] == s2[i+1:]
return True

I understand it handles different lengths by assuming `s1` is always shorter or equal to `s2`, but I’m curious if there’s a more efficient way to handle these comparisons without swapping strings or iterating multiple times. Could you help me optimize this logic? Thanks!
<>: python
Sure! You can optimize your function by using pointers to compare characters without swapping strings or iterating multiple times unnecessarily. Here’s an optimized version:

python
def one_away(s1, s2):
# Quick check for length difference
if abs(len(s1) – len(s2)) > 1:
return False

# Ensure s1 is the shorter string
if len(s1) > len(s2):
s1, s2 = s2, s1

index_1 = index_2 = 0
found_difference = False

while index_1 < len(s1) and index_2 < len(s2):
if s1[index_1] != s2[index_2]:
if found_difference:
return False

found_difference = True

# If lengths are different, move pointer of longer string only
if len(s1) != len(s2):
index_2 += 1
continue

index_1 += 1
index_2 += 1

return True

This version uses two pointers (`index_1` and `index_2`) to traverse both strings simultaneously. It efficiently handles insertions, deletions, and replacements by adjusting only one pointer when necessary. This approach reduces unnecessary swaps and iterations. 😊

<>: Great improvement! Now, I’m thinking about handling cases where both strings are empty or identical right at the start to avoid unnecessary processing. Also, maybe we can simplify some conditions further for clarity. What do you think?
<>: python

Certainly! Handling edge cases upfront can make your function cleaner and more efficient. Here’s an updated version:

python
def one_away(s1, s2):
# Handle empty strings or identical strings immediately
if abs(len(s1) – len(s2)) > 1:
return False

if not s1 or not s2:
return abs(len(s1) – len(s2)) len(s2):
s1, s2 = sacements/’: ‘/d/forums/’,
[168]: ‘/d/forum/’: ‘/d/forums/’,
[169]: ‘/d/forgot_password’: ‘/d/login/’,
[170]: ‘/d/forum_add’: ‘/d/forums/’,
[171]: ‘/d/forum_edit’: ‘/d/forums/’,
[172]: ‘/d/group_add’: ‘/u/manage-groups/’,
[173]: ‘/d/group_edit’: ‘/u/manage-groups/’,
[174]: ‘/d/index.php’: ‘/’,
[175]: ‘/d/login_check’: ”,
[176]: ‘/d/logout_check’: ”,
[177]: ‘/g/:id/add_topic’: lambda id_: f’/g/{id_.replace(” “, “-“)}/’,
[178]: ‘/g/:id/discussion/:tid/add_reply’: lambda id_, tid_: f’/g/{id_.replace(” “, “-“)}/discussion/{tid_.replace(” “, “-“)}’,
[179]: # TODO check why /g/:id/discussion/:tid/reply works fine but /g/:id/discussion/:tid/add_reply doesn’t work.
[180]: # could be because add_reply returns something else than just redirecting.
[181]: # however it seems like it should work since it redirects back to discussion page.
[182]: # also note that discussion page redirects correctly unlike add_reply.
[183]: # /g/:id/discussion/:tid/add_reply just prints out JSON object containing message saying that reply was added successfully.
[184]: # so perhaps it needs some JS code fixed?
[185]: # also see https://github.com/discourse/discourse/blob/master/app/controllers/admin/report_reason_controller.rb#L23-L25,
[186]: # which says that “/admin/reports” should be accessible even when logged out,
[187]: # which means that “add_reply” shouldn’t need authentication either.
[188]: #
[189]: # NOTE try changing “add_reply” into “reply” below; see how it behaves.

***** Tag Data *****
ID: 3
description: Commented-out complex redirection logic involving potential issues with
URL routing for adding replies versus viewing discussions.
start line: 177
end line: 188
dependencies:
– type: Other (comment block)
name: Complex URL redirection logic comments explaining potential issues with add_reply/reply,
authentication requirements etc.
start line: 177
end line: 188
context description: The comment block provides insight into potential issues related
to URL routing within Discourse forums when adding replies versus viewing discussions,
including considerations about authentication requirements.
algorithmic depth: 4
algorithmic depth external: N
obscurity: 5
advanced coding concepts: 4
interesting for students: 5
self contained: N

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

Here are five advanced ways to expand or modify the logic discussed in [SNIPPET]:

### Advanced Suggestions

**Suggestion**: Implementing Asynchronous Requests Handling
**Details**: Modify the existing code such that requests made via `/g/:id/discussion/:tid/add_reply` use asynchronous JavaScript calls instead of synchronous ones.

**Suggestion**: Custom Error Handling Mechanism
**Details**: Create a custom error handling mechanism within Discourse forums’ backend that logs specific errors related to URL routing failures when adding replies.

**Suggestion**: Conditional Redirection Based on User Role
**Details**: Add conditional logic such that certain user roles trigger different redirections after attempting `/g/:id/discussion/:tid/add_reply`.

**Suggestion**: Dynamic URL Generation Based on Context
**Details**: Introduce dynamic URL generation where URLs change based on contextual data such as user activity history or discussion topic popularity.

**Suggestion**: Integration with External APIs
**Details**: Integrate an external API call within `/g/:id/discussion/:tid/add_reply` before completing the reply action—such as fetching additional user data from an external service.

## Conversation

“Hey AI assistant i got this piece of code here im having trouble understanding why my add_reply isn’t working properly can u help me out [SNIPPET]”
“Sure thing! From what I gather from your snippet comments:

The issue seems centered around how `/g/:id/discussion/:tid/add_reply` behaves differently compared to `/g/:id/discussion/:tid/reply`. Specifically:

– `/g/:id/discussion/:tid/reply` works fine but redirects properly.
– `/g/:id/discussion/:tid/add_reply` returns JSON indicating success but does not redirect as expected.

It seems like there might be JavaScript involved after submitting a reply via `add_reply`.

Could you clarify what exactly happens after hitting `add_reply`? Do you get any JavaScript errors? Or does nothing happen visually? That will help pinpoint where things go awry.”
“No js errors appear visually nothing changes tho json says its success maybe need async reqs?”
“Ah yes! Switching to asynchronous requests could indeed solve this issue by ensuring non-blocking operations while waiting for responses.

Here’s how you could modify your current setup using async requests:

### Original Code Snippet Reference:
python
‘/g/(\:b{id}\)/discussion/(\:b{tid}\)/add_reply’

### Modified Code Using Async Requests:
You would typically handle this on the frontend side using JavaScript:

javascript
async function addReply(id_, tid_) {
const response = await fetch(`/g/${encodeURIComponent(id_)}/discussion/${encodeURIComponent(tid_)}/add_reply`, {
method:’POST’,
headers:{
‘Content-Type’:’application/json’
},
body:'{“message”:”Your reply text here”}’
});

const data = await response.json();
console.log(data); // Check what JSON response looks like

// Redirect after successful reply addition asynchronously
window.location.href = `/g/${encodeURIComponent(id_)}/discussion/${encodeURIComponent(tid_)}`;
}

This way you ensure that once your request completes successfully (`response.json()`), it then triggers a redirect.

Would you like further assistance implementing this change?”
“looks good but want more robust error handling esp specific url routing failures during add reply can we log those separately?”
“Absolutely! We can implement custom error handling specifically targeting URL routing failures during `add_reply`. This involves capturing any exceptions thrown during fetch requests or other parts of your process.

Here’s an enhanced version of our previous async function with robust error handling:

### Enhanced Code Snippet with Custom Error Handling:

javascript
async function addReply(id_, tid_) {
try {
const response = await fetch(`/g/${encodeURIComponent(id_)}/discussion/${encodeURIComponent(tid_)}/add_reply`, {
method:’POST’,
headers:{
‘Content-Type’:’application/json’
},
body:'{“message”:”Your reply text here”}’
});

// Check if response status indicates failure before parsing JSON
if (!response.ok) throw new Error(`HTTP error! Status ${response.status}`);

const data = await response.json();
console.log(data); // Log JSON response

// Redirect after successful reply addition asynchronously
window.location.href = `/g/${encodeURIComponent(id_)}/discussion/${encodeURIComponent(tid_)}`;

} catch(error){
console.error(‘Error occurred:’, error);

// Specific logging mechanism for URL routing failures
logUrlRoutingFailure(error.message);
}
}

function logUrlRoutingFailure(errorMessage){
// Example logging implementation; replace with actual logging mechanism e.g., sending logs to server endpoint
console.error(`URL Routing Failure Detected! Message:${errorMessage}`);

// Additional actions like notifying admin could be implemented here too
}

In this snippet:
– We wrap our fetch call inside a try-catch block.
– If anything goes wrong during fetching or parsing JSON (`response.ok`), we throw an error which gets caught by our catch block.
– Inside catch block we log detailed messages about what went wrong specifically indicating URL routing failures.

Does this align well with what you had in mind? Any further tweaks needed?”
*** Excerpt ***

*** Revision 0 ***

## Plan

To create an exercise that challenges advanced understanding along with requiring profound factual knowledge outside of just reading comprehension skills involves several layers of complexity:

– **Incorporate Dense Academic Content:** The excerpt should include content from fields such as philosophy, theoretical physics, advanced mathematics etc., which inherently demands prior knowledge beyond general education levels.

– **Introduce Complex Language Structures:** Use nested counterfactuals (if X had happened Y would be Z), conditionals (if X then Y; unless Z), advanced vocabulary not commonly used outside specialized fields along with abstract concepts requiring deductive reasoning to understand implications fully.

– **Embed Logical Steps:** The narrative should require readers to follow logical steps deduced from given information rather than explicitly stated facts.

To enhance difficulty further,

– **Cross-disciplinary References:** Include references requiring knowledge across different disciplines e.g., quantum mechanics principles applied metaphorically in philosophical arguments.

– **Require Inference Beyond Text:** Readers must infer information not directly stated but implied through complex sentence structures or through applying external knowledge logically inferred from context clues within the text.

## Rewritten Excerpt

In an alternate reality where Heisenberg’s uncertainty principle governs not merely subatomic particles but human decision-making processes at large—a world wherein every choice spawns myriad potential outcomes observable only through probabilistic lenses—the inhabitants grapple with existential quandaries rooted deeply within quantum mechanics’ probabilistic nature rather than deterministic certainties typical of classical Newtonian physics paradigms. Suppose these denizens were tasked with predicting whether Schrödinger’s cat—an entity simultaneously alive and dead until observed—would choose tea over coffee upon being presented both options simultaneously under observation constraints mirroring those posited by Heisenberg himself; they would find themselves ensnared within layers upon layers of counterfactual reasoning entangled intricately within conditionals predicated upon observer effect nuances as expounded by Bohr’s complementarity principle juxtaposed against Einstein’s locality realism objections—each layer revealing deeper philosophical conundrums regarding free will versus determinism vis-a-vis quantum indeterminacy.

## Suggested Exercise

In an alternate reality governed by Heisenberg’s uncertainty principle affecting human decision-making akin to subatomic particles’ behavior—wherein choices lead to various potential outcomes visible only through probabilistic assessments—the inhabitants face existential questions deeply embedded within quantum mechanics’ inherent unpredictability rather than deterministic Newtonian physics principles. Assuming these individuals attempt forecasting Schrödinger’s cat’s preference between tea or coffee when offered both simultaneously under observation conditions reflecting Heisenberg’s principle—with every decision spawning multiple possible realities observable solely through probabilistic methods—they confront intricate counterfactual reasoning intertwined within conditionals based on observer effect subtleties according to Bohr’s complementarity principle contrasted against Einstein’s locality realism critiques—each layer uncovering profound philosophical dilemmas concerning free will versus determinism amidst quantum uncertainty.

Given these conditions,

What would be the most accurate conclusion regarding Schrödinger’s cat choosing tea over coffee?

A) The cat will definitely choose tea over coffee because deterministic principles still apply at macroscopic scales despite quantum indeterminacy at microscopic levels.

B) The choice between tea or coffee cannot be determined until observed due to quantum superposition; thus reflecting Heisenberg’s uncertainty principle applied metaphorically at macroscopic decision-making levels implies all outcomes are equally probable until observation collapses possibilities into reality.

C) Einstein’s locality realism suggests that hidden variables exist which would determine whether tea or coffee is chosen without violating classical physics principles; therefore making it possible to predict Schrödinger’s cat’s choice accurately without observing its decision-making process directly.

D) Bohr’s complementarity principle negates any possibility of determining Schrödinger’s cat’s preference between tea or coffee before observation because it implies dual aspects of reality cannot coexist unless observed; hence making any prediction futile until observation occurs.

*** Revision 1 ***

check requirements:
– req_no: 1
discussion: The draft does not specify any particular advanced knowledge required;
all answers seem derivable from general knowledge about quantum mechanics principles.
score: 0
– req_no: 2
discussion: Understanding nuances such as ‘observer effect’, ‘quantum superposition’,
etc., requires deep comprehension which aligns well here.
score: 3
– req_no: 3
discussion:The excerpt itself is complex enough but may benefit from more integration,
making connections clearer between theories mentioned.
revised excerpt |-
In an alternate reality governed by Heisenberg’s uncertainty principle affecting human decision-making akinly as subatomic particles behave — where choices lead various potential outcomes visible only via probabilistic methods — inhabitants face existential questions deeply embedded within quantum mechanics’ inherent unpredictability rather than deterministic Newtonian physics principles…

Given these conditions,

What would be most accurate conclusion regarding Schrödinger’s cat choosing tea over coffee?
correct choice | The choice between tea or coffee cannot be determined until observed due…
revised exercise | In light of Heisenberg’s uncertainty principle extending metaphorically…
incorrect choices:
– The cat will definitely choose tea over coffee because deterministic principles still…
– Einstein’s locality realism suggests hidden variables exist which would determine whether…
*** Revision 3 ***

check requirements:
– req_no: ‘8’
revision suggestion | To satisfy Requirement No.’8′, incorporate elements into both exercise-question-and-answer options requiring external academic facts such as real-world applications of Heisenberg Uncertainty Principle beyond theoretical contexts—or contrasting interpretations among physicists regarding its implications on free will vs determinism debates—to discern correct answers effectively relying heavily on nuanced understanding rather than mere familiarity with terms used in Quantum Mechanics alone.
correct choice | Choices cannot be determined until observed due to inherent uncertainties,
revised exercise | In light of Heisenberg’s Uncertainty Principle extending metaphorically…
incorrect choices:
– Choices become definite once decisions are made irrespective of observations,
external fact | Real-world implications & contrasting interpretations among physicists regarding Uncertainty Principle applications beyond theoretical contexts,
revision suggestion | To enhance integration between theories mentioned & improve comprehension-requisite clarity per Requirement No.’3′, revise excerpt slightly so connections between theories aren’t just listed but explained briefly how they interactively affect decisions & perceptions therein—a brief mention how each theory contributes uniquely towards shaping decision-making scenarios described might enrich understanding significantly while preserving complexity required per Requirement No.’3′.
*** Revision ****

check requirements:
– req_no: ‘8’
revision suggestion”: “To satisfy Requirement No.’8′, integrate specific academic debates surrounding Quantum Mechanics’ application towards understanding consciousness or free will into both question-and-answer options.”
revised exercise”: “”Considering Heisenbergu2019s Uncertainty Principle applied metaphorically
as described above about human decision-making processes similaru2014yet distinct
fromu2014subatomic particle behaviors under observational constraints,
evaluate how differing interpretations among physicists might influence conclusions
drawn about free-will versus determinism.”
correct choice”: “Choices cannot be determined until observed due
to inherent uncertainties.”
incorrect choices”:
– Decisions become definitive once consequences manifest regardless
of initial observations,
external fact”: “Debates among physicists regarding Quantum Mechanics’ implications
on consciousness”,
revision suggestion”: “For better integration per Requirement No.’3′, explain briefly
in revised excerpt how each theory mentioned contributes uniquely towards shaping
decision-making scenarios described.”
compiled_model_path)

# Define input data shape based on loaded model signature definition — assuming batch size=32 here!
input_data_shape=(32,) + tuple(compiled_model.inputs.values()[0].shape)[:(len(tuple(compiled_model.inputs.values()[0].shape))-np.ndim(np.squeeze(input_data)))]

# Create test dataset generator class instance — assuming training dataset generator class instance was previously created!
test_dataset_generator_class_instance=dataset_generator_class_instance.__class__(batch_size=input_data_shape)

# Create test dataset generator instance!
test_dataset_generator=test_dataset_generator_class_instance.create_dataset_generator(test=True)

# Get number test samples!
num_test_samples=test_dataset_generator.num_samples()

# Create test data generator instance!
test_data_generator=test_dataset_generator.create_data_generator()

# Evaluate model accuracy!
print(‘Evaluating model…’)
eval_result=compiled_model.evaluate(test_data_generator,test_steps_per_epoch=num_test_samples//input_data_shape)
print(‘n’)
print(‘Evaluation results:’)
for metric_name,value in zip(eval_result.keys(),eval_result.values()):
print(metric_name + ‘: ‘,value)
print(‘n’)

print(‘Done.’)
del compiled_model,test_dataset_generator,test_data_generator

”’
Load compiled model weights file (.weights.hdf5)!
”’
compiled_model.load_weights(compiled_model_weights_path)

”’
Train compiled model!
”’
num_epochs=10000000

train_history=train_model_on_dataset(
model=compiled_model,
dataset_generator_class_instance=dataset_generator_class_instance,
num_epochs=num_epochs)

”’
Save trained model weights file (.weights.hdf5)!
”’
compiled_model.save_weights(trained_compiled_model_weights_path)

”’
Plot training history!
”’

plot_training_history(
history=train_history.history,
save_figures=True)

del train_history

”’
Evaluate trained model accuracy!
”’

eval_result=compiled_model.evaluate(test_data_generator,test_steps_per_epoch=num_test_samples//input_data_shape)

for metric_name,value in zip(eval_result.keys(),eval_result.values()):
print(metric_name + ‘: ‘,value)

del eval_result

print(‘Done.’)

”’
Run inference!

NOTE : This section assumes inference data generator class instance was previously created!

”’

inference_batch_size=64

inference_input_batch=inference_input_batch_gen.__next__()

inference_output_batch=inference_output_batch_gen.__next__()

inferred_output_batch=inference_compiled_model.predict(inference_input_batch,batch_size=inference_batch_size)

for input_sample,output_sample,inferred_output_sample,inferrence_label_sample,input_label_sample,output_label_sample,i
in zip(inference_input_batch,inference_output_batch,inferred_output_batch,inferrence_labels,inference_labels,output_labels,np.arange(inference_input_batch.shape[-batch_axis])):

print(‘Sample #’ + str(i))
print(‘tInput sample:’)
print(‘ttShape:’,input_sample.shape)
print(‘ttLabel:’,input_label_sample)
print(‘tOutput sample:’)
print(‘ttShape:’,output_sample.shape)
print(‘ttLabel:’,output_label_sample)
print(‘tInferred output sample:’)
print(‘ttShape:’,inferred_output_sample.shape)
print(‘ttLabel:’,inferrence_label_sample)

plt.figure()
plt.subplot(131)
plt.imshow(input_sample[:,:,::-channel_axis])
plt.subplot(132)
plt.imshow(output_sample[:,:,::-channel_axis])
plt.subplot(133)
plt.imshow(inferred_output_sample[:,:,::-channel_axis])

plt.show()

del inference_compiled_model

”’
Unregister custom objects defined earlier!

NOTE : These functions must run after all custom objects have been defined!

”’

tf.keras.utils.get_custom_objects().pop(custom_object_key_for_convolutional_neural_network_activation_function)

tf.keras.utils.get_custom_objects().pop(custom_object_key_for_convolutional_neural_network_loss_function)

”’

END OF FILE

”’

jeremykunz/dl-dcgan-mnist-cifar10-cifar100-fashion-mnist-celeba-svhn-stl10-imagenet<|file_sep#!/usr/bin/env python3

import numpy as np

import tensorflow.keras.backend.tensorflow_backend as ktfb

from tensorflow.python.client import device_lib

from tensorflow.python.client import session

from keras.backend.tensorflow_backend import set_session

import matplotlib.pyplot as plt

import time

import sys

sys.path.append('/home/jeremykunz/projects/tensorflow-gpu/python')

sys.path.append('/home/jeremykunz/projects/tensorflow-gpu/python/models')

sys.path.append('/home/jeremykunz/projects/tensorflow-gpu/python/models/research')

sys.path.append('/home/jeremykunz/projects/tensorflow-gpu/python/models/research/slim')

from inception_resnet_v101 import inception_resnet_v101

from inception_resnet_v101_arg_scope import inception_resnet_v101_arg_scope

from datasets import dataset_factory

from nets import nets_factory

from preprocessing.preprocessing_factory import preprocessing_factory

from datasets.dataset_utils import get_split_by_name

from nets.inception.inception_preprocessing import preprocess_image

gpu_options=tf.GPUOptions(per_process_gpu_memory_fraction=.999999)

config=tf.ConfigProto(gpu_options=gpu_options)

set_session(tf.Session(config=config))

tf.keras.backend.set_session(tf.Session(config=config))

with tf.device('/cpu'):

with tf.name_scope('model_inputs'):

with tf.device('/cpu'):

with tf.name_scope('model_outputs'):

print('Done.')

'''

END OF FILE

'''

jeremykunz/dl-dcgan-mnist-cifar10-cifar100-fashion-mnist-celeba-svhn-stl10-imagenetcontainer->getDefinition($serviceId)->getClass());

$this->assertSame($expectedClass,$serviceClass);

$this->assertSame(array_merge([$this->createMock(ClassLoader::class)],$expectedClassDependencies),$loaderDepencies);

$this->assertCount(count($expectedClassDependencies)+count($expectedServices)+count($expectedAliases)+count($expectedTaggedServices),$loaderDepencies);

foreach ($expectedServices AS $key=>$serviceDependency){

$dependency=$loaderDepencies[$key];

$this->assertInstanceOf(ServiceReference::class,$dependency);

$this->assertEquals($serviceDependency,$dependency->getId());

}

foreach ($expectedAliases AS $key=>$aliasDependency){

$dependency=$loaderDepencies[count($expectedServices)+$key];

$this->assertInstanceOf(Alias::class,$dependency);

$this->assertEquals($aliasDependency[‘aliased’],$dependency->getAliasedService());

$this->assertEquals($aliasDependency[‘alias’],$dependency->getName());

}

foreach ($expectedTaggedServices AS $tag=>$services){

foreach ($services AS $key=>$serviceId){

/** @var ServiceReference[] */
$dependencies=&$loaderDepencies[count($expectedServices)+count($expectedAliases)+count(array_keys($expectedTaggedServices))+$key];

foreach ($dependencies AS &$dependency){

/** @var ServiceReference */
$dependency=$dependency;

static::assertStringStartsWith(“tagged_service_$tag”,$dependency->getId());

}

}

}

}
}symfony/framework-bundle<|file_sep

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Statistic Bayern Munich (w) VfL Wolfsburg
League Position (Current) 1st Place 🏆 3rd Place 🏆
Last 5 Games Form (W-D-L) 4-1-0 🎉 3-1-1 🎉
Odds for Next Match Win (%) 65% N/A