Yes, using self-hosted runners for your GitHub Actions workflows can indeed help you get around the limitation in GitHub-hosted minutes. GitHub Actions provides a certain number of minutes for automation tasks on GitHub-hosted runners, depending on the plan you have (e.g., public repositories on free accounts have unlimited minutes, whereas private repositories have a limited number). These limitations do not apply to self-hosted runners.

When you use a self-hosted runner, the computation is done on your own machines, and you are responsible for the maintenance and cost of these machines. This means that all of the processing time spent on actions using your own infrastructure does not count toward the GitHub Actions minute quotas imposed on GitHub-hosted runners.

Here's what you should consider when using self-hosted runners:

1. **Setup and Maintenance**: You will need to set up, maintain, and update the runner machines yourself. This includes ensuring the security of the tokens and software, handling system updates, and managing any third-party software or dependencies required for your workflows.

2. **Scale**: Depending on your workload, you may need to manage multiple runners or even implement an autoscaling solution to ensure you have enough capacity while keeping costs under control.

3. **Security**: Since the runner will have access to your code, you need to ensure that it's securely set up, especially if it is processing code from public repositories, as it can then be at risk from malicious pull requests.

4. **Cost**: While you bypass the minute limitations, you must account for the operating costs of your servers or computing resources.

5. **Network**: The runner needs to be able to communicate with GitHub to receive webhook events for workflow execution. Make sure your networking setup doesn't block this communication.

To add a self-hosted runner, you'll need to go to the 'Settings' tab of your repository, then 'Actions' and 'Runners', where you'll find instructions to add and configure your own runner. Remember to follow GitHub's recommendations for using self-hosted runners safely, especially when it comes to repository access and the scope of permissions granted to the runner.

In summary, self-hosted runners are a great way to overcome the GitHub Actions runtime limitations on GitHub-hosted runners, but they come with an overhead of setup, maintenance, and potentially, cost.

In the television series "Breaking Bad," Jack Welker, the leader of a neo-Nazi gang, kills Hank Schrader to protect his own interests and those of Walter White, also known as "Heisenberg." Hank, who is a DEA agent and Walter's brother-in-law, has finally caught Walter and is ready to bring him in for his crimes in the methamphetamine manufacturing and distribution ring.

The pivotal moment comes in the episode titled "Ozymandias," which is the third to last of the series. Walter had previously called Jack and his gang for help when he realized Hank and his partner Steve Gomez had tracked him down to his money's hiding place. However, once Walter sees that Hank has been captured, he tries to plead for Hank's life, offering all his money to Jack in exchange for letting Hank go.

Jack executes Hank instead because he sees him as a liability that could dismantle his criminal operations. From Jack's perspective, Hank knows too much about their illegal activities, and even if Jack accepted Walter's money in exchange for Hank's life, there would be no guarantee that Hank would not pursue them later. Additionally, by this point in the series, Hank has made it clear that he would never cut a deal and would attempt to bring everyone involved in the meth operation to justice, including his own brother-in-law.

Hank's death is a crucial moment in the series, setting in motion the final events leading to the conclusion of the show. It demonstrates the ultimate consequences of Walter's choices and his descent into the criminal underworld, illustrating that no one, not even family, is safe from the repercussions of his actions. It also reinforces the cold, ruthless nature of Jack and the world Walter has become entangled with.

In summary, Jack kills Hank because he perceives him as an undeniable threat to his and Walter's illegal ventures, regardless of the money offered and the family ties at stake. It's a stark portrayal of the tragic fallout from Walter's journey into moral corruption.

It seems like you are using a Debian-based system (like Ubuntu) and trying to install Python packages in an environment that is externally managed, meaning that the system's package manager controls the Python environment and packages instead of using `pip`.

Here's what you can do:

1. **System-wide Package Installation with `apt`:**
If you are trying to install commonly used Python packages, they might be available in your system's repositories. You can install these using the `apt` package manager:
```
sudo apt install python3-xyz
```
Replace `xyz` with the name of the package you want to install. This is the preferred method if you are managing Python packages that should be available to all system users or are dependencies for other system software.

2. **Creating a Virtual Environment:**
For development purposes or if you need a specific version of a package, use a Python virtual environment. This allows you to install packages without affecting the system-wide Python installation:
```
sudo apt install python3-full
python3 -m venv path/to/venv
source path/to/venv/bin/activate
pip install xyz
```
Replace `path/to/venv` with the directory you want your virtual environment to reside in, and replace `xyz` with the package you are trying to install.

3. **Install Python Applications with `pipx`:**
If you're looking to install Python applications rather than libraries, you can use `pipx` which will automatically create a separate virtual environment for each application:
```
sudo apt install pipx
pipx install xyz
```
Where `xyz` is the Python application you'd like to install. `pipx` is particularly useful if you want to avoid version conflicts between different Python applications or if you want to isolate your Python applications from the system Python.

Remember to always replace `xyz` with the actual name of the package or application you are trying to install.

Lastly, note that using `sudo` with `pip` is not recommended as it can lead to security risks and could potentially interfere with system-managed packages. Always prefer to work in a virtual environment when working with Python projects to avoid these issues.

Before proceeding, please refer to `/usr/share/doc/python3.12/README.venv` for more information specific to your version of Python, as the error message suggests.

The question about "pgadmin storage manager file location" seems to be inquiring about where PostgreSQL or pgAdmin stores certain files, such as backups, configuration files, or saved data. Since the question lacks specific details, I'll provide a broad answer which should be applicable to several scenarios.

**pgAdmin Configuration Files**:

pgAdmin, the popular graphical administration tool for PostgreSQL, stores its configuration files in different locations depending on the operating system:

- **On Windows**: Configuration files are often found in `%APPDATA%\pgAdmin\` or within the installation directory, which by default is in `Program Files`.

- **On macOS**: Look for configuration files in the user's home directory under `~/Library/Application Support/pgAdmin/`.

- **On Linux**: Configuration files are typically located in `~/.pgadmin/` or `/var/lib/pgadmin`.

**PostgreSQL Data Directory**:

PostgreSQL data directory, where all the data files and configurations are stored, is specified in the `postgresql.conf` file. To find the data directory, you can also run the following command from the SQL shell (`psql`):

```sql
SHOW data_directory;
```

The result will be the path to the data directory.

**pgAdmin Storage Manager**:

If by "storage manager" you are referring to where pgAdmin stores files and backups when you use its backup and restore features, this is typically determined at the time you perform the operation. When you back up a database using pgAdmin, you'll be asked to specify a filename and a location where the backup file will be saved.

By default, pgAdmin may use the last location you selected or offer the default downloads or documents location of your operating system. But it's important to understand that this is user-determined.

Remember that for server backups, pgAdmin runs the `pg_dump` or `pg_dumpall` utility, and for restores, it uses `pg_restore`. These utilities work with file paths specified when you run them. If you are using the command line to execute these utilities, you'll need to specify the paths directly.

For evergreen advice, ensure that you:

- Know your PostgreSQL version, as file locations and default settings can change between versions.
- Regularly backup your databases and configuration files.
- Securely manage your backup files, especially since they may contain sensitive data.
- Consult the official pgAdmin documentation or PostgreSQL documentation for the most accurate and updated information.

Always check the documentation for the specific version of pgAdmin and PostgreSQL you are using, as directory structures and default behaviors can change with updates.

The `ImportError` you're encountering is indicating that the `libGL.so.1` shared library, which is required for OpenGL support, is missing on your system. This issue is common on Linux operating systems, especially when you're trying to run applications that depend on OpenGL for graphics rendering.

Here's how you can resolve the problem:

For Ubuntu/Debian-based distributions, you can install the required libraries using the following command:

```bash
sudo apt-get update
sudo apt-get install libgl1-mesa-glx
```

For Red Hat/CentOS/Fedora distributions, the command will be:

```bash
sudo yum install mesa-libGL
```

For Arch Linux or Manjaro, use the pacman package manager:

```bash
sudo pacman -Syu mesa
```

Once you have installed the required package, try running your application again. It should be able to find `libGL.so.1` and start without issues.

If you are using a Docker container or any other form of virtualized environment, you might need to ensure that the base image has the necessary libraries installed. For example, in a Dockerfile, you could add the following commands:

```dockerfile
FROM ubuntu:latest

RUN apt-get update && \
apt-get install -y libgl1-mesa-glx
```

Remember to rebuild your Docker container after updating the Dockerfile.

For Nvidia graphics card users, if the above doesn't work, you might need to install or reinstall Nvidia drivers, as they provide their own version of the `libGL.so.1` library. Use the package manager to install the Nvidia drivers or download them directly from the Nvidia website.

By resolving these dependencies, not only will you have fixed the immediate issue, but you're also ensuring that future users of the application on similar systems have a direct solution to the problem. It's important to note that the specific package and installation command can vary based on the Linux distribution and the version in use, so adapt the solution as necessary.

When you encounter the `UnicodeDecodeError: 'utf-8' codec can't decode byte 0x80 in position 0: invalid start byte` error, it typically indicates that you are attempting to read or decode a binary file (such as a file generated by the `pickle` module in Python) as if it were a text file encoded in UTF-8.

The `pickle` module is used for serializing and de-serializing a Python object structure. When you serialize an object with `pickle`, it is converted into a byte stream, which is not readable as standard UTF-8 encoded text. When you try to read this byte stream with the assumption that it is a UTF-8 text file, you will encounter the above error because the binary data does not conform to UTF-8 encoding standards.

Here is the correct way to use `pickle` to serialize and de-serialize objects:

To serialize (or "pickle") an object:

```python
import pickle

# Your object
my_object = {'key': 'value'}

# Serialize the object to a binary file
with open('my_object.pkl', 'wb') as output_file:
pickle.dump(my_object, output_file)
```

To de-serialize (or "unpickle") the object:

```python
import pickle

# Load the object back from the binary file
with open('my_object.pkl', 'rb') as input_file:
my_object = pickle.load(input_file)

print(my_object) # Output: {'key': 'value'}
```

Note that when opening the file for reading or writing, the mode is `'rb'` or `'wb'` for read binary or write binary, respectively.

Please ensure the following to avoid such errors:

1. **Correct usage of file modes**: Use binary mode 'rb' or 'wb' when dealing with binary files such as those created by `pickle`.

2. **Matching `pickle` version**: Ensure you are not trying to unpickle an object with a different Python version than it was pickled with. While `pickle` tries to maintain backward compatibility, objects pickled with a newer Python version may not be unpickle-able with older versions.

3. **File corruption**: Make sure that the file has not been corrupted or altered, as this can also cause the error.

4. **Right file**: Make sure you are trying to unpickle the correct file and not accidentally a different file which is not a pickled object.

If you're sharing pickled data between computers or storing it for long-term use, it's worth noting that `pickle` is not secure against erroneous or maliciously constructed data. Consider using a more robust format such as `json` (for data interchange) or `numpy`'s `.npz` for numerical data if this is a concern, although these alternatives cannot serialize arbitrary Python objects as comprehensively as `pickle`.

The error `TypeError: unsupported operand type(s) for +: ‘QuerySet’ and ‘QuerySet’` in Django occurs because you are trying to combine two QuerySet objects using the `+` operator, which is not supported by Django ORM (Object-Relational Mapping).

To combine results from two QuerySets into a single list, you can use Python's list concatenation, as follows:

```python
queryset1 = Model1.objects.filter(...)
queryset2 = Model2.objects.filter(...)

combined_list = list(queryset1) + list(queryset2)
```
However, if both QuerySets are of the same model, or if they are different models but you still want to work with them as QuerySets (for example, to perform further filtering or ordering), you can use the `|` operator to create a combined QuerySet using Django's `Q` object:

```python
from django.db.models import Q

queryset1 = Model.objects.filter(...)
queryset2 = Model.objects.filter(...)

combined_queryset = queryset1 | queryset2
```

If you need to combine QuerySets of different models and still want to use it as a QuerySet (perhaps for further annotations, aggregations, or if they share a common interface), you can consider using `union()`, which is a set operation that returns a new QuerySet containing objects from both QuerySets. Note that `union()` requires that the fields of the QuerySets are identical.

```python
combined_queryset = queryset1.union(queryset2)
```

Please keep in mind that `union()` does not allow for ordering across the combined sets and removes duplicates by default (you can pass `all=True` to `union()` if you want to keep duplicates).

Finally, if these solutions are not suitable for your needs, perhaps because you need to maintain a complex ordering, or require additional processing that cannot be done within the ORM, you might consider iterating over both QuerySets and handling the combination manually in your Python code.

Remember that using list concatenation or combining QuerySets with `|` or `union()` could have performance implications, as they may execute multiple queries depending on the use case, and might not be suitable for very large QuerySets. Always evaluate the efficiency of your approach in the context of your specific application and dataset sizes.

When you encounter the error:

```
NotSupportedError: (1235, “This version of MariaDB doesn’t yet support ‘LIMIT & IN/ALL/ANY/SOME subquery'")
```

This indicates that you are trying to execute an SQL command in MariaDB that includes a subquery with a `LIMIT` clause within an `IN`, `ALL`, `ANY`, or `SOME` comparison operation, but the version of MariaDB you are using does not support this feature.

As of my knowledge cutoff in early 2023, such limitations still existed in MariaDB and even in MySQL, which MariaDB is a fork of. Here are some ways to work around this issue:

1. **Refactor the Query:**
Often, the need for a `LIMIT` clause within a subquery can be avoided by restructuring the query. For example, you can often join tables or use a derived table (subquery in the `FROM` clause) to achieve the same result without using `LIMIT` in the subquery.

2. **Use an Ordered Subquery in a Join:**
If the goal was to limit rows from one table based on criteria from another, you might use a derived table joined to the main table. In the derived table, you can include an `ORDER BY` and `LIMIT`, and then join this back to the main table on the relevant criteria.

Example:
```sql
SELECT main.*
FROM main_table AS main
JOIN (
SELECT id
FROM related_table
ORDER BY some_column
LIMIT n -- your limit here
) AS sub ON main.related_id = sub.id
```

3. **Variable Row Numbering:**
In some cases, particularly when you need the first 'n' related items, you can simulate a limit by using session variables to assign row numbers, and then filter on this number.

Example:
```sql
SELECT *
FROM (
SELECT t.*,
(@row_number:=IF(@prev_value=t.related_criteria, @row_number+1, 1)) AS rn,
@prev_value:=t.related_criteria
FROM (SELECT @row_number:=0, @prev_value:=NULL) AS vars, target_table t
ORDER BY t.related_criteria, t.some_other_column
) AS rows
WHERE rn <= n -- your limit here
```

4. **Upgrade MariaDB:**
While earlier versions of MariaDB and MySQL have this limitation, sometimes newer versions reduce or eliminate such restrictions. Review the release notes and documentation of newer MariaDB versions to determine if upgrading is a viable solution.

5. **Use Temporary Tables:**
In more complex scenarios, or when performance is not the primary concern, results can be stored in a temporary table with an index on the columns you wish to apply the `LIMIT` to. Then, select the limited data from the temporary table as part of the larger query.

Remember to always analyze and understand the implications of these workarounds in the context of your specific use case and to test for performance and accuracy.

It's also important for anyone stumbling upon this answer in the future to check the current MariaDB or MySQL documentation in case there have been updates that change the support for subqueries with `LIMIT`.

The original "Toy Story" movie, released in 1995, takes place primarily in a suburban neighborhood and features several key locations that serve as important backdrops for the story's events. Below are the main locations depicted in the film:

1. **Andy's Room**: This is where the story begins, and it is the primary setting for a lot of the film's action. Andy's room is where Woody, Buzz, and the rest of the toys live and interact when Andy is not around.

2. **The Rest of Andy's House**: Various scenes take place in other parts of Andy's house, including the living room, hallway, and the yard. The house is the setting for birthday parties, encounters with the family dog (Scud), and the toys' adventurous journey from one room to another.

3. **Sid's Room**: Sid is the film's antagonist, a child who lives next door to Andy and is notorious for torturing and dismantling toys for fun. His room is a stark contrast to Andy's, filled with broken toys and toy parts, and it is where Woody and Buzz encounter a group of "mutant" toys.

4. **Sid's House**: In addition to Sid's room, other parts of his house, like the hallway and backyard, serve as scenes for the toys' endeavors to escape and save themselves from Sid's destructive habits.

5. **Pizza Planet**: This is a space-themed restaurant to which Woody and Buzz hitch a ride, attempting to return to Andy. It's a key location filled with arcade games and a claw machine ("The Claw"), where the aliens reside and where Buzz believes he can find a spaceship to take him back to his home planet.

6. **The Moving Truck**: Towards the end of the film, the toys have to chase down a moving truck that is taking the rest of the toys (and Andy's family) to their new home. Various dramatic rescue scenes occur around and inside the truck.

Each setting is instrumental to the development of the plot and characterization, and the unique environments helped establish "Toy Story" as a groundbreaking film in the realm of animated storytelling. It's a testament to the profound creativity of Pixar Animation Studios and a primary reason for the film's enduring popularity.

AMD uses the Intel instruction set architecture (ISA), primarily because it ensures compatibility with the vast majority of software written for personal computers, which has historically been designed for the x86 architecture created by Intel.

The x86 ISA became the dominant standard for desktop and laptop computers since the 1980s due to the widespread adoption of IBM's personal computers, which were powered by Intel's x86 processors. This created a positive feedback loop, with software developers focusing on x86 to ensure their programs could reach the largest possible market, and users purchasing x86-based systems to run the widest range of software.

In 1982, AMD signed a contract with Intel to become a second-source manufacturer of x86 processors, which legally allowed AMD to produce processors that were compatible with Intel's technology. This relationship was integral in AMD's ability to build CPUs that could compete in the market with compatibility for the popular operating systems and applications that were designed to run on Intel's x86 processors.

As technology evolved, both companies engaged in cross-licensing agreements, and AMD introduced the x86-64 extension (also known as AMD64), which extended the x86 architecture to 64-bit. Intel eventually adopted a compatible version of this extension under the name Intel 64 (formerly EM64T), further entangling their ecosystems.

Ensuring compatibility with the x86 (and consequently x86-64) ISA allows AMD to participate in the broader PC market, leveraging existing software and ecosystems without forcing users or software developers to deal with compatibility issues that would arise from a different, incompatible ISA. This compatibility has been a major factor in AMD's success in the consumer, server, and enterprise markets, where software compatibility is a fundamental requirement.

In essence, the use of the Intel instruction set by AMD enables it to create CPUs that are directly competitive with Intel's offerings, often differentiating on performance, price, or features, while maintaining the essential compatibility that the market demands.