Introduction to the Exciting World Cup Qualification Matches
The basketball world is abuzz with anticipation as the World Cup Qualification for America's 1st Round Group B approaches. With international teams vying for a spot in the prestigious tournament, fans and experts alike are eagerly awaiting the matches scheduled for tomorrow. This guide provides an in-depth look at the teams, key players, and expert betting predictions to enhance your viewing experience.
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Overview of Group B Teams
- Team A: Known for their aggressive defense and strategic gameplay, Team A has consistently performed well in previous qualifiers. Their star player, John Doe, is renowned for his exceptional shooting accuracy and leadership on the court.
- Team B: With a focus on speed and agility, Team B excels in fast breaks and quick transitions. Their point guard, Jane Smith, is a pivotal player whose vision and ball-handling skills make her a formidable opponent.
- Team C: Team C boasts a strong lineup with a balanced mix of experienced veterans and promising young talents. Their center, Mike Johnson, is known for his defensive prowess and ability to dominate the paint.
- Team D: Emphasizing teamwork and cohesion, Team D relies on their collective effort rather than individual stars. Their coach has implemented innovative strategies that have led to surprising victories against stronger opponents.
Key Players to Watch
John Doe - Shooting Guard
John Doe's ability to score from anywhere on the court makes him a critical asset for his team. His recent performances have shown an impressive improvement in three-point shooting, making him a top contender for MVP this season.
Jane Smith - Point Guard
Jane Smith's court vision and leadership are unparalleled. Her ability to orchestrate plays and distribute the ball effectively keeps her team ahead in high-pressure situations.
Mike Johnson - Center
Mike Johnson's presence in the paint is intimidating for any opponent. His defensive skills are complemented by his ability to score efficiently close to the basket.
Sarah Lee - Small Forward
Sarah Lee's versatility allows her to adapt to various roles on the court. Her defensive tenacity and offensive creativity make her one of the most dynamic players in Group B.
Match Predictions and Betting Insights
Prediction: Team A vs Team B
This matchup promises to be a thrilling encounter between two evenly matched teams. Experts predict that Team A's strong defense will counterbalance Team B's speed, leading to a closely contested game. Betting odds favor Team A slightly due to their home-court advantage.
- Betting Tip: Consider placing bets on under 200 total points due to both teams' defensive capabilities.
- Predicted Outcome: Team A wins by a narrow margin of 5 points.
Prediction: Team C vs Team D
In this clash of styles, Team C's experience might give them an edge over Team D's youthful energy. However, Team D's innovative strategies could disrupt Team C's rhythm. The game is expected to be highly competitive with unpredictable moments.
- Betting Tip: Place bets on over 210 total points as both teams have potent offensive capabilities.
- Predicted Outcome: A close victory for Team C by 7 points.
Prediction: Key Player Performances
Betting enthusiasts should keep an eye on individual performances that could sway game outcomes significantly. John Doe is anticipated to score over 25 points against Team B, while Jane Smith is expected to achieve double-digit assists against Team A.
- Betting Tip: Bet on John Doe scoring more than 25 points due to his consistent scoring streaks.
- Betting Tip: Consider betting on Jane Smith achieving at least 10 assists given her role as playmaker for her team.
Tactical Analysis of Upcoming Matches
Tactics Employed by Top Teams
[0]: import os
[1]: import sys
[2]: import time
[3]: import numpy as np
[4]: # Import pybullet modules
[5]: import pybullet_data
[6]: import pybullet as p
[7]: # Import other modules
[8]: from gym.spaces.box import Box
[9]: from environments.pybullet.racecar_env import RacecarEnv
[10]: class RacecarEnvContinuous(RacecarEnv):
[11]: """
[12]: This class implements continuous control.
[13]: """
[14]: def __init__(self,
[15]: max_episode_steps=1000,
[16]: render=False,
[17]: timestep=0.01,
):
def __init__(self,
max_episode_steps=1000,
render=False,
timestep=0.01,
use_xy_action=True,
car_mass=1000.,
car_max_speed=20.,
wheel_base=1.,
steering_range=np.radians(40),
max_steer_angle=np.radians(30),
friction_coefficient=1.,
target_velocity=None):
self.use_xy_action = use_xy_action
self.car_mass = car_mass
self.car_max_speed = car_max_speed
self.wheel_base = wheel_base
self.steering_range = steering_range
self.max_steer_angle = max_steer_angle
self.friction_coefficient = friction_coefficient
if target_velocity is None:
target_velocity = np.random.uniform(low=self.car_max_speed * .75,
high=self.car_max_speed)
self.target_velocity = target_velocity
print("target velocity", target_velocity)
super().__init__(
max_episode_steps=max_episode_steps,
render=render,
timestep=timestep)
# Define action space (continuous control)
if not use_xy_action:
low_action_bound = np.array([np.radians(-max_steer_angle), -1])
high_action_bound = np.array([np.radians(max_steer_angle), 1])
action_space_shape = (len(low_action_bound),)
else:
low_action_bound = np.array([-1., -1., -np.pi / 4])
high_action_bound = np.array([+1., +1., +np.pi / 4])
action_space_shape = (len(low_action_bound),)
self.action_space = Box(
low=low_action_bound,
high=high_action_bound)
# Define observation space (continuous state)
observation_space_shape=(8,)
low_observation_bound=-np.inf * np.ones(observation_space_shape)
high_observation_bound=np.inf * np.ones(observation_space_shape)
self.observation_space=Box(
low=low_observation_bound,
high=high_observation_bound)
def reset(self):
obs=self._reset()
return obs
def step(self,a):
obs,r,t,_=self._step(a)
return obs,r,t,_
def _reset(self):
p.resetSimulation()
p.setGravity(0,0,-10)
p.setTimeStep(self.timestep)
def _step(self,a):
if not self.use_xy_action:
steer=a[0]
throttle=a[-1]
vx_desired=self.target_velocity*throttle
if steer<-self.max_steer_angle or steer >self.max_steer_angle:
raise ValueError('Invalid steering angle')
else:
steer=max(min(steer,self.max_steer_angle),-self.max_steer_angle)
vx_desired=max(min(vx_desired,self.car_max_speed),-self.car_max_speed)
def _get_obs(self):
x,y,z=p.getBasePositionAndOrientation(self.car_id)
orn=p.getEulerFromQuaternion(p.getBasePositionAndOrientation(self.car_id)[1])
vx,vx_x,vx_z=p.getBaseVelocity(self.car_id)[0]
roll,pitch,yaw=p.getEulerFromQuaternion(p.getBasePositionAndOrientation(self.car_id)[1])
***** Tag Data *****
ID: 5
description: Complex `_step` method handling vehicle dynamics including steering constraints
and velocity calculations.
start line: 68
end line: 104
dependencies:
- type: Method
name: _step
start line: 68
end line: 104
context description: Handles each step of simulation including applying actions (steering/throttle),
enforcing constraints like maximum steering angle/velocity limits.
algorithmic depth: 4
algorithmic depth external: N
obscurity: 4
advanced coding concepts: 4
interesting for students: 5
self contained: N
************
## Challenging aspects
### Challenging aspects in above code
#### Algorithmic Depth:
- **Steering Angle Constraints**: The code enforces strict boundaries on steering angles which can lead students into understanding how physical constraints affect motion planning algorithms.
- **Velocity Constraints**: Similarly constraining velocities ensures that students grasp how these limits affect overall system dynamics.
- **Non-linear Control Inputs**: Handling non-linear inputs such as `vx_desired` based on `throttle` introduces complexity when dealing with dynamic systems.
#### Logical Complexity:
- **Conditional Logic**: The conditional checks ensure that only valid actions are processed; otherwise an error is raised.
- **Vector Operations**: The extraction of velocities (`vx`, `vx_x`, `vx_z`) involves understanding vector components within physical simulations.
- **Physics Simulation Integration**: Integrating physics engines like PyBullet requires deep understanding of both software interfaces (`getBasePositionAndOrientation`, `getEulerFromQuaternion`, etc.) and underlying physics principles.
### Extension
#### Specific Extensions:
- **Dynamic Target Velocity**: Instead of having a fixed `target_velocity`, introduce scenarios where it changes dynamically based on environmental conditions or internal states.
- **Additional Constraints**: Implement additional constraints such as lateral acceleration limits or tire slip angles which add realism but also complexity.
- **Adaptive Control Mechanism**: Introduce adaptive mechanisms where parameters like `max_steer_angle` or `car_max_speed` change based on real-time feedback from sensors.
## Exercise
### Problem Statement:
Expand upon [SNIPPET] by implementing advanced control features within your simulation environment:
**Requirements**:
1. **Dynamic Target Velocity**:
- Modify `_step` method such that `target_velocity` can vary dynamically based on certain conditions (e.g., proximity sensors detecting obstacles).
2. **Lateral Acceleration Constraint**:
- Introduce lateral acceleration limits (`lat_acc_limit`). Ensure that during each step calculation, lateral accelerations do not exceed this limit.
3. **Adaptive Max Steer Angle**:
- Implement an adaptive mechanism where `max_steer_angle` adjusts based on current velocity (`vx`). For example, reduce maximum steer angle at higher speeds.
4. **Logging Mechanism**:
- Add logging functionality that records key variables (`steer`, `throttle`, `vx_desired`, etc.) at each step into a structured format (e.g., CSV file).
### Code Snippet Reference:
Refer back to [SNIPPET] provided earlier.
### Detailed Solution:
python
import numpy as np
class AdvancedCarSimulator:
def __init__(self):
# Initialize parameters here...
self.target_velocity_functionality_on = True # Example flag for dynamic target velocity feature activation.
self.lat_acc_limit = ... # Set some realistic value here e.g., lateral acceleration limit in m/s^2.
...
def _dynamic_target_velocity(self):
# Implement logic here...
if some_condition_based_on_proximity_sensors():
return new_dynamic_target_velocity_value()
return default_target_velocity_value()
def _calculate_lateral_acceleration(self):
x,y,z,p,q,r=p.getBasePositionAndOrientation(self.car_id)
_,_,vy,pitch,rpy,pqr=p.getBaseVelocityOrientationAngularVelocityLinearAcceleration(self.car_id)
lat_acc=np.sqrt(vy**2 + r**2) # Simplified formula; refine based on actual requirements!
return lat_acc
def _adaptive_steering_limits(self,vx):
base_steering_limit=self.max_steer_angle_at_low_speeds()
adjustment_factor=vx/self.high_reference_speed() # Adjust according to specific requirements!
adjusted_steering_limit=max(base_steering_limit*(1-adjustment_factor),min_allowed_steering_value())
return adjusted_steering_limit
# Main snippet function implementation with extensions...
def _step(self,a):
# Original snippet code...
if not self.use_xy_action:
# Original snippet code...
else:
# Original snippet code...
# New Dynamic Target Velocity Implementation...
if self.target_velocity_functionality_on == True :
steer=a[0]
throttle=a[-1]
dynamic_target_vel=self._dynamic_target_velocity()
vx_desired=dynamic_target_vel*throttle
if steer<-self._adaptive_steering_limits(vx) or steer >self._adaptive_steering_limits(vx):
raise ValueError('Invalid steering angle')
else :
steer=max(min(steer,self._adaptive_steering_limits(vx)),-self._adaptive_steering_limits(vx))
vx_desired=max(min(vx_desired,self.car_max_speed),-self.car_max_speed)
lat_acc=self._calculate_lateral_acceleration()
if lat_acc > self.lat_acc_limit :
raise ValueError('Lateral acceleration limit exceeded')
# Logging mechanism addition...
log_data={'steer':steer,'throttle':throttle,'vx_desired':vx_desired,'lat_acc':lat_acc}
with open('simulation_log.csv','a') as f:
f.write(','join(map(str,map(log_data.values())))+'n')
## Follow-up exercise:
### Additional Challenge:
Implement collision detection within your simulation environment using proximity sensors data integrated into `_step`. If collision detected log it separately into another file named 'collision_log.csv'.
### Solution:
python
def _detect_collision(self):
collision_detected=False
proximity_threshold_value=some_threshold_value()
proximity_sensor_data=p.getProximitySensorData()
for sensor_reading in proximity_sensor_data :
if sensor_reading>: Hi there! I'm working with this Python function called `_get_pytables_multi_group`. It seems designed for reading multiple groups from PyTables files using parallel processing via multiprocessing pools or serially if needed.
Here's the code snippet:
python
def _get_pytables_multi_group(filenames, node_name_list=None, table_name_list=None,
group_path='/', modes=None, attrs=None):
'''
Parameters
----------
filenames : list-like object containing strings
a list of filenames.
node_name_list : list-like object containing strings or None values,
optional(default=None)
If None then tables are loaded directly under root directory level.
Otherwise you can specify what nodes you want to load.
Note that table names get appended to the node names so you do not needto include them.
I'm curious about how exactly I should structure my input lists like `node_name_list` or `table_name_list`. Could you help clarify how these parameters work together? I'm feeling a bit lost trying to figure out their roles! Thanks!
<>: python
Sure! Let's break it down:
- **`node_name_list`:** This parameter allows you to specify specific nodes within each PyTables file from which you want to load data. If you set it to `None`, it will default to loading tables directly under the root directory level of each file.
- **`table_name_list`:** This parameter specifies which tables you want within those nodes. If you provide node names in `node_name_list`, these table names will be appended automatically under those nodes.
Here’s how they work together:
1. If both are provided:
- For each filename, it will look under each specified node (from `node_name_list`) and attempt to load each specified table (from `table_name_list`) within those nodes.
2. If only one is provided:
- If only `node_name_list` is provided without specifying tables explicitly in your operation logic elsewhere (like appending table names), it assumes all tables under those nodes should be loaded.
- If only `table_name_list` is provided without nodes specified in `node_name_list`, it assumes these tables exist directly under the root directory level.
This setup gives you flexibility depending on your data structure needs! 😊 Let me know if there’s anything else you’d like clarified!
<>: Got it! Now I'm thinking about making this function more robust by adding error handling for cases where files might be missing or corrupted during parallel processing. Also, I'd like it to handle different file formats more gracefully instead of assuming everything is perfectly formatted PyTables files. How can I integrate these improvements?
<>: To enhance robustness with error handling and support for different file formats, consider these steps:
1. **Error Handling for Missing/Corrupted Files:**
- Use try-except blocks around file operations within your function.
- Log errors or warnings when files are missing or corrupted instead of letting exceptions crash your program.
python
import logging
logging.basicConfig(level=logging.WARNING)
def safe_open_file(filename):
try:
return pd.HDFStore(filename) # Assuming PyTables usage here; adjust accordingly.
except Exception as e:
logging.warning(f"Failed to open {filename}: {e}")
return None
2. **Handling Different File Formats Gracefully:**
- Check file extensions before attempting operations specific to PyTables.
- Use conditional logic or helper functions tailored for different formats.
python
def process_file(filename):
if filename.endswith('.pytables'):
store = safe_open_file(filename)
if store is not None:
# Process using PyTables logic here.
pass
else:
logging.warning(f"Unsupported file format for {filename}")
3. **Integrate into Your Function:**
- Modify `_get_pytables_multi_group` to use these helper functions when iterating over filenames.
By implementing these changes, your function will become more resilient against unexpected issues while being adaptable across different scenarios! 😊 Let me know if you need further assistance!
<>: Great suggestions! Now, I'm thinking about adding retry logic with exponential backoff when opening files fails due to temporary issues like network glitches or resource locks. This should help make the function even more robust during parallel processing tasks where transient errors might occur frequently.
How can I implement this retry mechanism effectively within the existing structure?
<>: To implement retry logic with exponential backoff effectively, you can use Python's `time.sleep()` along with a loop structure that attempts retries after increasing delays between attempts.
Here's how you can integrate this into your existing structure:
python
import time
import logging
logging.basicConfig(level=logging.WARNING)
def safe_open_file_with_retry(filename, max_retries=5):
attempt = 0
delay = 1 # Initial delay time in seconds
while attempt <= max_retries:
try:
store = pd.HDFStore(filename) # Assuming PyTables usage here; adjust accordingly.
return store
except Exception as e:
attempt += 1
logging.warning(f"Attempt {attempt} failed for {filename}: {e}")
if attempt > max_retries:
logging.error(f"Failed after {max_retries} attempts")
return None
time.sleep(delay)
delay *= 2 # Exponential backoff
def process_file(filename):
if filename.endswith('.pytables'):
store = safe_open_file_with_retry(filename)
if store is not None:
# Process using PyTables logic here.
pass
else:
logging.warning(f"Unsupported file format for {filename}")
### Key Points:
- **Exponential Backoff:** The delay doubles after each failed attempt (`delay *= 2`). You can adjust initial delay and maximum retries according to your needs.
- **Logging:** Provides feedback on each attempt and logs an error after exhausting all retries.
This approach helps manage transient errors gracefully during parallel processing tasks! 😊 Let me know if there’s anything else you’d like help with!
<>: Awesome! Now let's integrate this retry logic into `_get_pytables_multi_group`. We should wrap the file opening part with this retry mechanism so that any temporary issues don't cause immediate failures during parallel processing tasks. How would you suggest structuring this integration?
<>: python
To integrate retry logic into `_get_pytables_multi_group`, wrap the file opening part using the retry mechanism we discussed earlier. Here’s how you can structure it:
python
import pandas as pd
def _get_pytables_multi_group(filenames, node_name_list=None, table_name_list=None,
group_path='/', modes=None, attrs=None):
"""
Parameters remain unchanged...
"""
def safe_open_file_with_retry(filename, max_retries=5):
attempt = 0
delay = 1
while attempt <= max_retries:
try:;
store pd.HDFStore(filename);
return store;
except Exception e;
attempt += ;
logging.warning(f“Attempt {attempt} failed fo{filename}: {e}”);
if attempt > max_retries:;
logging.error(f“Failed after {max_retries} attempts”);
return None;
time.sleep(delay);
delay *= ;
def process_file(filename);
if filename.endswith('.pytables');
store safe_open_file_with_retry(filename);
if store != None:;
# Process using PyTables logic here.;
else:;
logging.warning(f“Could not open {filename} after retries.”);
for filename in filenames:;
process_file(filename);
### Integration Highlights:
- **Retry Logic:** The function now uses `safe_open_file_with_retry()` when attempting to open files within `_get_pytables_multi_group`.
- **Error Handling:** Logs warnings when retries fail after reaching maximum attempts without crashing immediately due to transient issues.
This integration ensures resilience against temporary issues during parallel processing tasks! 😊 Let me know if there’s anything else you need help with!
<>: Perfect! Now let's make sure we're handling different node structures dynamically within `_get_pytables_multi_group`. We should iterate over potential nodes specified in `node_name_list` and apply our retry logic when accessing them too. How can we adjust our current setup to accommodate varying node structures efficiently?
<> To handle varying node structures dynamically within `_get_pytables_multi_group`, iterate over potential nodes specified in `node_name_list`. Apply retry logic when accessing these nodes too by wrapping access operations similarly.
Here’s how you can adjust your setup:
python
import pandas as pd
def _get_pytables_multi_group(filenames, node_name_list=None, table_name_list=None,
group_path='/', modes=None, attrs=None):
"""
Parameters remain unchanged...
"""
def safe_access_node(store_or_table_node_with_retry(node_or_table_path,max_retries=5)):
_attempt_=_attempt_=0_
_delay_=delay_=_delay_=1_
while_attempt_<=_max_retries_:_;
try:;
returnstore_or_table_node_[node_or_table_path];
except Exception_e_:_;
_attempt_+=_;
logging.warning(_f_Attempt_{attempt}_failed_for_{node_or_table_path}:_{e}_");
if_attempt_>max_retries_:;
logging.error(_f_Failed_after_{max_retries}_attempts_);
returnNone_;
time.sleep(_delay_);
_delay_*=_delay_*=_delay_*=_delay_=;
def process_file_and_nodes(filename,node_names_if_any=[]);;
if filename.endswith('.pytables');
store=safe_open_file_with_retry(filename);
if_store!=None:;
for_node_in_(node_names_if_anyor_[['']]):;
node=safe_access_node(store,node_in_);
if_node!=None:;
for_table_in_(table_names_if_anyor_[['']]):;
table=safe_access_node(node[table_in_]);
if_table!=None:;
# Process table data here.;
else:;
logging.warning(_f_Could_not_access_{table_in}_in_{node_in}_after_retries.);
else:;
logging.warning(_f_Could_not_access_{node_in}_after_retries.);
else:;
logging.warning(_f_Could_not_open_{filename}_after_retries.);
else:;
logging.warning(_f_Unsupported_format_for_{filename}.);
for_filename_in_(filenames);;
process_file_and_nodes(filename,node_namelist_);
### Key Adjustments:
- **Node Access Retry:** Introduced `safe_access_node()` similar to file access retry logic but applied per node/table path access attempt.
- **Dynamic Node Iteration:** Iterates over potential nodes specified in `node_name_list`.
This setup efficiently manages varying node structures while maintaining resilience against transient issues! 😊 Let me know if there’s anything else!
<>: Looks solid! I think we've covered everything I needed for now. Thanks for guiding me through this—I'll start integrating these changes into my project!
<>: Sure thing! I'm glad I could help guide you through enhancing your function with robust error handling and dynamic node management strategies. Best of luck integrating these changes into your project! If you have any more questions down the road or need further assistance—feel free reach out anytime!
Happy coding! 🚀<>: Hi there,
I'm trying out some custom loss functions involving KL divergence computation between Gaussian distributions using TensorFlow Probability distributions API layers inside Keras models' call methods.
Here's what my code looks like right now:
python
class KLDivergenceLayer(tf.keras.layers.Layer): ### Define KL-Divergence Layer ###
def __init__(self,P,Q,chls=[],name='kl_divergence_layer'):
super(KLDivergenceLayer,self).__init__(name=name)
self.P = tf.keras.layers.InputLayer(input_shape=(P.shape[-1],)) ## Portion layer P ##
self.Q = tf.keras.layers.InputLayer(input_shape=(Q.shape[-1],)) ## Portion layer Q ##
def call(self,input_features): ## Run Layer ##
x := input_features[:,:,:,:input_features.shape[-1]//2] ## X-Axis features ##
y := input_features[:,:,:,input_features.shape[-1]//2:] ## Y-Axis features ##
p_distr := tfd.MultivariateNormalDiag(loc=self.P(x)) ## P Distribution ##
q_distr := tfd.MultivariateNormalDiag(loc=self.Q(y)) ## Q Distribution ##
return tf.reduce_sum(tfd.kl_divergence(p_distr,q_distr)) ## Kullback-Leibler Div ##
However, when I run my model training loop using this layer definition above (where P,Q are pre-trained networks trained beforehand just used here), TensorFlow complains about something related shapes mismatching somewhere deep inside TF Probability distribution API layers... but honestly speaking its really hard tracing what went wrong exactly since its deep inside TF API itself..
Here's what I get upon running my training loop::
Traceback (most recent call last):
File "train_model.py", line XX In call(input_features): -> tfa.distributions.kl_divergence(p_distr,q_distr))
tensorflow.python.framework.errors_impl.InvalidArgumentError:[...] logits dimension must be greater than zero [...]
[[{{function_node __wrapped__KLDivergenceLayer_call_device_/job:xla_worker/replica...}}]]
During handling of [...], another exception occurred [...]:
ValueError:[...] incompatible shape between distribution inputs [...]
I suspect there might be something wrong with how I've set up my distributions P & Q but honestly at this point am unsure what exactly needs changing.. Any guidance would be greatly appreciated!
Thanks!
Best regards,
Confused Developer 😅🤔🐍🙏🏽✨✨✨✨✨✨✨✨✨✨✨✨✨✨✨ ✍🏽💻💼📈💡😎👩💻👩💻🧠💪🏼😁😁😁😁😁😁😁😁😁😁😁😁😁❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️☺️☺️☺️☺️☺️☺️☺️☺️☺️ ☀⛅⛈⚡⛈⚡⚡⛈⛅⚡⚡⛅ ☀ ⛅ ☀ ☀ ☀ ☀ ☀ ☀ ☀ ☀ ☀ 🌞 🌞 🌞 🌞 🌞 🌞 🌞 🌞 🌞 🌞 🔥 🔥 🔥 🔥 🔥 🔥 🔥 🔥 🔥 🔥 ⭐ ⭐ ⭐ ⭐ ⭐ ⭐ ⭐ ⭐ ⭐ ⭐ ✍🏽 💻 💼 💼 💼 💼 💼 💼 💼 💼 💼 ✍🏽 ✍🏽 ✍🏽 ✍🏽 ✍🏽 ✍🏽 ✍🏽 ✍🏽 ✍♂ 👩💻 👩💻 👩💻 👩💻 👩💻 👩💻 👩💻 👩💻 👩💻 👩💻 👱♀ ♀ ♀ ♀ ♀ ♀ ♀ ♀ ♀ ❣ ❣ ❣ ❣ ❣ ❣ ❣ ❣ ❣ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ♡ ♡ ♡ ♡ ♡ ♡ ♡ ♡ ♡ ♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥ 😀 😀 😀 😀 😀 😀 😀 😀 😀 😉 😉 😉 😉 😉 😉 😉
Confused Developer 😅🙏🐇♂︎✔︎✔︎✔︎✔︎✔︎✔︎✔︎✔︎ ✔︎ ✔︎ ✔︎ ✔︎ ✔︎ ✔︎ ✔︎ ✔ @@@@@@@@@@@@ @@ @@ @@ @@ @@ @@ @@ @@ @@ @@
