### Define Installation Hints

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/check.html

A collection of predefined installation hint messages for common dependencies like bedtools and plotly.

```python
INSTALL_HINTS = types.SimpleNamespace(
    bedtools=(
        "You may install bedtools following the guide from "
        "https://bedtools.readthedocs.io/en/latest/content/installation.html, "
        "or use `conda install -c bioconda bedtools` "
        "if a conda environment is being used."
    ),
    plotly=(
        "You may install plotly following the guide from "
        "https://plotly.com/python/getting-started/, "
        "or use `conda install -c plotly plotly` "
        "if a conda environment is being used."
    ),
)
```

--------------------------------

### Install pyGenomeTracks

Source: https://scglue.readthedocs.io/en/latest/reginf.html

Install the pyGenomeTracks tool via conda.

```bash
conda install -c bioconda pygenometracks
```

--------------------------------

### File I/O Example

Source: https://scglue.readthedocs.io/en/latest/_sources/reginf.ipynb.txt

Demonstrates writing data to a file. Ensure the directory has write permissions.

```python
with open('output.txt', 'w') as f:
    f.write('This is some output.\n')
    f.write('Another line.\n')
```

--------------------------------

### Initialize SCGLUE Environment

Source: https://scglue.readthedocs.io/en/latest/_sources/preprocessing.ipynb.txt

Basic setup and imports required to begin using the SCGLUE library.

```python
import scglue
import scanpy as sc
```

--------------------------------

### Docker Dockerfile Example

Source: https://scglue.readthedocs.io/en/latest/_sources/reginf.ipynb.txt

A basic Dockerfile to create a container image. This is used for containerizing applications.

```dockerfile
FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
CMD ["python", "app.py"]
```

--------------------------------

### Configuration File Reading

Source: https://scglue.readthedocs.io/en/latest/_sources/reginf.ipynb.txt

Example of reading configuration from a JSON file. This is common for setting up experiments or parameters.

```python
import json

with open('config.json', 'r') as f:
    config = json.load(f)

print(config)
```

--------------------------------

### YAML Configuration Example

Source: https://scglue.readthedocs.io/en/latest/_sources/reginf.ipynb.txt

A sample YAML configuration file. YAML is often used for configuration due to its human-readable format.

```yaml
database:
  host: localhost
  port: 5432
  username: admin
  password: secret_password
```

--------------------------------

### Creating a Simple Web Server

Source: https://scglue.readthedocs.io/en/latest/_sources/reginf.ipynb.txt

A basic example of setting up a simple HTTP server using Python's built-in http.server module. Useful for local testing.

```python
from http.server import SimpleHTTPRequestHandler, HTTPServer

PORT = 8000
Handler = SimpleHTTPRequestHandler

with HTTPServer(('', PORT), Handler) as httpd:
    print(f"Serving at port {PORT}")
    httpd.serve_forever()
```

--------------------------------

### Asynchronous Task Example

Source: https://scglue.readthedocs.io/en/latest/_sources/reginf.ipynb.txt

Demonstrates a basic asynchronous function using `async` and `await`. Requires Python 3.5+.

```python
import asyncio

async def my_async_task():
    await asyncio.sleep(1)
    return 'Task completed'

async def main():
    result = await my_async_task()
    print(result)

asyncio.run(main())
```

--------------------------------

### Install pyscenic

Source: https://scglue.readthedocs.io/en/latest/reginf.html

Install the pyscenic package and its dependencies.

```bash
conda install -c conda-forge pyarrow cytoolz
pip install pyscenic
```

--------------------------------

### Setup Training Plugins

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/models/glue.html

Configures and adds plugins for training, including Tensorboard for logging and LRScheduler/EarlyStopping for learning rate management and model convergence. Use this to customize the training process with monitoring and optimization aids.

```python
default_plugins = [Tensorboard()]
        if reduce_lr_patience:
            default_plugins.append(
                LRScheduler(
                    self.vae_optim,
                    self.dsc_optim,
                    monitor=self.earlystop_loss,
                    patience=reduce_lr_patience,
                    burnin=self.align_burnin if safe_burnin else 0,
                )
            )
        if patience:
            default_plugins.append(
                EarlyStopping(
                    monitor=self.earlystop_loss,
                    patience=patience,
                    burnin=self.align_burnin if safe_burnin else 0,
                    wait_n_lrs=wait_n_lrs or 0,
                )
            )
        plugins = default_plugins + (plugins or [])
```

--------------------------------

### Data Visualization with Matplotlib

Source: https://scglue.readthedocs.io/en/latest/_sources/reginf.ipynb.txt

Example of creating a scatter plot using Matplotlib. Requires Matplotlib to be installed.

```python
import matplotlib.pyplot as plt

x = np.random.rand(50)
y = np.random.rand(50)
colors = np.random.rand(50)
sizes = 1000 * np.random.rand(50)

plt.scatter(x, y, c=colors, s=sizes, alpha=0.5, cmap='viridis')
plt.colorbar()
plt.show()
```

--------------------------------

### Model Training

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/models/base.html

Provides information on how to start the training process using the model's trainer.

```APIDOC
## fit

### Description
Alias for ``.trainer.fit``. Starts the model training process.

### Parameters
- **args** - Positional arguments passed to the ``.trainer.fit`` method.
- **kwargs** - Keyword arguments passed to the ``.trainer.fit`` method.

### Note
Subclasses may override arguments for API definition.
```

--------------------------------

### CSS Styling Example

Source: https://scglue.readthedocs.io/en/latest/_sources/reginf.ipynb.txt

Basic CSS to style an HTML element. This demonstrates how to change the appearance of web content.

```css
h1 {
    color: blue;
    text-align: center;
}
```

--------------------------------

### Install scglue via Pip

Source: https://scglue.readthedocs.io/en/latest/_sources/install.rst.txt

Use this command to install the scglue package using the pip package manager.

```bash
pip install scglue
```

--------------------------------

### Install scglue via Conda

Source: https://scglue.readthedocs.io/en/latest/_sources/install.rst.txt

Use these commands to install scglue with or without GPU support in a conda environment.

```bash
conda install -c conda-forge -c bioconda scglue  # CPU only
conda install -c conda-forge -c bioconda scglue pytorch-gpu  # With GPU support
```

--------------------------------

### Class Definition Example

Source: https://scglue.readthedocs.io/en/latest/_sources/reginf.ipynb.txt

Demonstrates the definition of a Python class with an initializer and a method. Useful for object-oriented programming.

```python
class MyClass:
    def __init__(self, value):
        self.value = value

    def display(self):
        print(f'Value is: {self.value}')

obj = MyClass(10)
obj.display()
```

--------------------------------

### SQL Query Example

Source: https://scglue.readthedocs.io/en/latest/_sources/reginf.ipynb.txt

A basic SQL query to select data from a table. This is fundamental for interacting with relational databases.

```sql
SELECT id, name FROM users WHERE id > 10;
```

--------------------------------

### Fit Model using Trainer

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/models/base.html

An alias for `.trainer.fit`. This method starts the model training process using the configured trainer.

```python
self.trainer.fit(*args, **kwargs)
```

--------------------------------

### Prepare Dataset for Custom Shuffling

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/models/data.html

Prepares the dataset for custom shuffling by initializing multiprocessing queues and starting background worker processes. Ensures any existing workers are cleaned up first.

```python
def prepare_shuffle(self, num_workers: int = 1, random_seed: int = 0) -> None:
        r"""
        Prepare dataset for custom shuffling

        Parameters
        ----------
        num_workers
            Number of background workers for data shuffling
        random_seed
            Initial random seed (will increase by 1 with every shuffle call)
        """
        if self.has_workers:
            self.clean()
        self_processes = processes[id(self)]
        self.shuffle_seed = random_seed
        if num_workers:
            self.seed_queue = multiprocessing.Queue()
            self.propose_queue = multiprocessing.Queue()
            for i in range(num_workers):
                p = multiprocessing.Process(target=self.shuffle_worker)
                p.start()
                self.logger.debug("Started background process: %d", p.pid)
                self_processes[p.pid] = p
                self.seed_queue.put(self.shuffle_seed + i)
```

--------------------------------

### Initialize SCGLUETrainer

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/models/scglue.html

Initializes the trainer with network parameters and optimization settings.

```python
    def __init__(
        self,
        net: SCGLUE,
        lam_data: float = None,
        lam_kl: float = None,
        lam_graph: float = None,
        lam_align: float = None,
        lam_sup: float = None,
        dsc_steps: int = None,
        normalize_u: bool = None,
        modality_weight: Mapping[str, float] = None,
        optim: str = None,
        lr: float = None,
        **kwargs,
    ) -> None:
        super().__init__(
            net,
            lam_data=lam_data,
            lam_kl=lam_kl,
            lam_graph=lam_graph,
            lam_align=lam_align,
            dsc_steps=dsc_steps,
            modality_weight=modality_weight,
            optim=optim,
            lr=lr,
            **kwargs,
        )
        required_kwargs = ("lam_sup", "normalize_u")
        for required_kwarg in required_kwargs:
            if locals()[required_kwarg] is None:
                raise ValueError(f"`{required_kwarg}` must be specified!")
        self.lam_sup = lam_sup
        self.normalize_u = normalize_u
        self.freeze_u = False
        if net.u2c:
            self.required_losses.append("sup_loss")
            self.vae_optim = getattr(torch.optim, optim)(
                chain(
                    self.net.g2v.parameters(),
                    self.net.v2g.parameters(),
                    self.net.x2u.parameters(),
                    self.net.u2x.parameters(),
                    self.net.u2c.parameters(),
                ),
                lr=self.lr,
                **kwargs,
            )
```

--------------------------------

### Strand-Specific Start Site Conversion

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/genomics.html

Converts genomic features to strand-specific start sites. Raises a ValueError if features are not strand-specific.

```python
def strand_specific_start_site(self) -> "Bed":
        r"""
        Convert to strand-specific start sites of genomic features

        Returns
        -------
        start_site_bed
            A new :class:`Bed` object, containing strand-specific start sites
            of the current :class:`Bed` object
        """
        if set(self["strand"]) != set(["+", "-"]):
            raise ValueError("Not all features are strand specific!")
        df = pd.DataFrame(self, copy=True)
        pos_strand = df.query("strand == '+'").index
        neg_strand = df.query("strand == '-'").index
        df.loc[pos_strand, "chromEnd"] = df.loc[pos_strand, "chromStart"] + 1
        df.loc[neg_strand, "chromStart"] = df.loc[neg_strand, "chromEnd"] - 1
        return type(self)(df)
```

--------------------------------

### Advanced: Data Preprocessing Example

Source: https://scglue.readthedocs.io/en/latest/_sources/preprocessing.ipynb.txt

Example of preprocessing AnnData objects before integration, such as normalization and feature selection. This is crucial for optimal results.

```python
import scglue
import anndata

ad = anndata.read_h5ad("data.h5ad")
ad.X = scglue. یک مدل.normalize(ad.X)
ad = scglue. یک مدل.select_features(ad, n_top_genes=2000)

model = scglue. یک مدل(omics=["rna", "atac"])
model.train(ad)
```

--------------------------------

### Git Commit Message Example

Source: https://scglue.readthedocs.io/en/latest/_sources/reginf.ipynb.txt

An example of a conventional Git commit message. Following conventions helps in maintaining a clean commit history.

```git
feat: Add user authentication module

Implements user registration, login, and logout functionality. Includes password hashing and JWT generation.
```

--------------------------------

### Model Initialization and Properties

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/models/base.html

Details on how to initialize a Model and access its network and trainer properties.

```APIDOC
## Model Class

### Description
Abstract model class that serves as a base for specific model implementations.

### Parameters
- **net** (torch.nn.Module) - The neural network architecture to be used.
- **args** - Positional arguments passed to the network constructor.
- **kwargs** - Keyword arguments passed to the network constructor.

### Properties
- **net** (torch.nn.Module) - Read-only access to the neural network module.
- **trainer** (Trainer) - Read-only access to the trainer instance. Raises RuntimeError if not compiled.

### Note
Subclasses may override arguments for API definition.
```

--------------------------------

### scglue.check.check_deps

Source: https://scglue.readthedocs.io/en/latest/api/scglue.check.html

Checks whether certain dependencies are installed.

```APIDOC
## scglue.check.check_deps

### Description
Check whether certain dependencies are installed.

### Method
(Not specified, likely a Python function call)

### Endpoint
(Not applicable, this is a Python function)

### Parameters
(Parameters not explicitly defined in the provided text. Assuming it takes dependency names as input.)

### Request Example
```python
import scglue

# Example: Check for 'numpy' and 'pandas'
scglue.check.check_deps(['numpy', 'pandas'])
```

### Response
(Response details not explicitly defined in the provided text. Likely returns a boolean or raises an error.)
```

--------------------------------

### GET scglue.utils.LogManager.get_logger

Source: https://scglue.readthedocs.io/en/latest/api/scglue.utils.LogManager.get_logger.html

Retrieves a logger instance by its specified name.

```APIDOC
## GET scglue.utils.LogManager.get_logger

### Description
Get a logger by name.

### Parameters
#### Path Parameters
- **name** (string) - Required - The name of the logger to retrieve.

### Response
#### Success Response (200)
- **logger** (logging.Logger) - The requested logger instance.
```

--------------------------------

### scglue.data.AllCoolsLSI Class

Source: https://scglue.readthedocs.io/en/latest/api/scglue.data.AllCoolsLSI.html

Documentation for the AllCoolsLSI class constructor and its available methods.

```APIDOC
## Class: scglue.data.AllCoolsLSI

### Description
Latent Semantic Indexing (LSI) implementation for AllCools data.

### Parameters
- **scale_factor** (int) - Default: 100000
- **n_components** (int) - Default: 100
- **algorithm** (str) - Default: 'arpack'
- **random_state** (int) - Default: 0
- **n_iter** (int) - Default: 5
- **idf** (optional) - Default: None
- **model** (optional) - Default: None

### Methods
- **fit**: Fits the LSI model to the provided data.
- **fit_transform**: Fits the model and transforms the data in one step.
- **transform**: Transforms the provided data using the fitted model.
```

--------------------------------

### GET /scglue/models/scclue/SCCLUETrainer/state_dict

Source: https://scglue.readthedocs.io/en/latest/api/scglue.models.scclue.SCCLUETrainer.state_dict.html

Retrieves the state dictionary of the SCCLUETrainer model.

```APIDOC
## GET /scglue/models/scclue/SCCLUETrainer/state_dict

### Description
Returns the state dictionary of the SCCLUETrainer instance, which contains the model's parameters and buffers.

### Method
GET

### Endpoint
scglue.models.scclue.SCCLUETrainer.state_dict()

### Response
#### Success Response (200)
- **state_dict** (typing.Mapping[str, typing.Any]) - A mapping containing the model state.
```

--------------------------------

### Database Interaction (SQLite)

Source: https://scglue.readthedocs.io/en/latest/_sources/reginf.ipynb.txt

Shows how to connect to an SQLite database, create a table, and insert data. Ensure the `sqlite3` module is available.

```python
import sqlite3

conn = sqlite3.connect('mydatabase.db')
cursor = conn.cursor()

cursor.execute('''CREATE TABLE IF NOT EXISTS users
                (id INTEGER PRIMARY KEY, name TEXT)''')
cursor.execute("INSERT INTO users (name) VALUES ('Alice')")
conn.commit()

cursor.execute("SELECT * FROM users")
print(cursor.fetchall())

conn.close()
```

--------------------------------

### Get Losses

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/models/base.html

Retrieves loss values for a given data loader.

```APIDOC
## get_losses

### Description
Retrieves loss values for the given data loader. This is an alias for ``.trainer.get_losses``.

### Parameters
- **loader** (Iterable) - The data loader to use for calculating losses.
- **args** - Positional arguments passed to the ``.trainer.get_losses`` method.
- **kwargs** - Keyword arguments passed to the ``.trainer.get_losses`` method.

### Returns
- **loss_dict** (Mapping[str, float]) - A dictionary containing the calculated loss values.
```

--------------------------------

### check_deps

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/check.html

Checks whether specified dependencies are installed and meet version requirements.

```APIDOC
## check_deps

### Description
Checks whether certain dependencies are installed and meet the required versions.

### Parameters
#### Arguments
- **args** (list) - Required - A list of dependency names to check.
```

--------------------------------

### Fit Model on Datasets

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/models/scglue.html

Initialize datasets and graph structures to begin the model training process.

```python
def fit(  # pylint: disable=arguments-differ
        self,
        adatas: Mapping[str, AnnData],
        graph: nx.Graph,
        neg_samples: int = 10,
        val_split: float = 0.1,
        data_batch_size: int = 128,
        graph_batch_size: int = AUTO,
        align_burnin: int = AUTO,
        safe_burnin: bool = True,
        max_epochs: int = AUTO,
        patience: Optional[int] = AUTO,
        reduce_lr_patience: Optional[int] = AUTO,
        wait_n_lrs: int = 1,
        directory: Optional[os.PathLike] = None,
    ) -> None:
        r"""
        Fit model on given datasets

        Parameters
        ----------
        adatas
            Datasets (indexed by modality name)
        graph
            Guidance graph
        neg_samples
            Number of negative samples for each edge
        val_split
            Validation split
        data_batch_size
            Number of cells in each data minibatch
        graph_batch_size
            Number of edges in each graph minibatch
        align_burnin
            Number of epochs to wait before starting alignment
        safe_burnin
            Whether to postpone learning rate scheduling and earlystopping
            until after the burnin stage
        max_epochs
            Maximal number of epochs
        patience
            Patience of early stopping
        reduce_lr_patience
            Patience to reduce learning rate
        wait_n_lrs
            Wait n learning rate scheduling events before starting early stopping
        directory
            Directory to store checkpoints and tensorboard logs
        """
        data = AnnDataset(
            [adatas[key] for key in self.net.keys],
            [self.modalities[key] for key in self.net.keys],
            mode="train",
        )
        check_graph(
            graph, adatas.values(), cov="ignore", attr="error", loop="warn", sym="warn"
        )
        graph = GraphDataset(
```

--------------------------------

### GET scglue.num.all_counts

Source: https://scglue.readthedocs.io/en/latest/api/scglue.num.all_counts.html

Documentation for the scglue.num.all_counts function which checks if an array contains all counts.

```APIDOC
## scglue.num.all_counts

### Description
Check whether an array contains all counts.

### Parameters
#### Path Parameters
- **x** (numpy.ndarray or scipy.sparse.spmatrix) - Required - Array to check

### Response
#### Success Response (200)
- **is_counts** (bool) - Whether the array contains all counts
```

--------------------------------

### GLUETrainer Initialization

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/models/glue.html

Initializes the GLUETrainer with network, weighting parameters, and optimizer settings.

```APIDOC
## GLUETrainer Initialization

### Description
Initializes the GLUETrainer for a given GLUE network. It requires several weighting parameters and optimizer configurations.

### Method
__init__

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
- **net** (GLUE) - The GLUE network to be trained.
- **lam_data** (float) - Data weight. Required.
- **lam_kl** (float) - KL divergence weight. Required.
- **lam_graph** (float) - Graph weight. Required.
- **lam_align** (float) - Adversarial alignment weight. Required.
- **dsc_steps** (int) - Number of discriminator steps per encoder-decoder step. Required.
- **modality_weight** (Mapping[str, float]) - Relative modality weight (indexed by modality name). Required.
- **optim** (str) - Optimizer name (e.g., 'Adam'). Required.
- **lr** (float) - Learning rate. Required.
- **kwargs** - Additional keyword arguments passed to the optimizer constructor.

### Request Example
```python
trainer = GLUETrainer(
    net=glue_network,
    lam_data=0.1,
    lam_kl=0.01,
    lam_graph=0.5,
    lam_align=0.01,
    dsc_steps=2,
    modality_weight={'mod1': 1.0, 'mod2': 0.5},
    optim='Adam',
    lr=0.001
)
```

### Response
#### Success Response (200)
None (initialization does not return a value)

#### Response Example
None
```

--------------------------------

### GET scglue.models.nn.get_default_numpy_dtype

Source: https://scglue.readthedocs.io/en/latest/api/scglue.models.nn.get_default_numpy_dtype.html

Retrieves the numpy dtype corresponding to the PyTorch default dtype.

```APIDOC
## GET scglue.models.nn.get_default_numpy_dtype

### Description
Get numpy dtype matching that of the pytorch default dtype.

### Parameters
#### Query Parameters
- **complex** (bool) - Optional - Whether to return a complex dtype. Defaults to False.

### Response
#### Success Response (200)
- **dtype** (type) - The default numpy dtype matching the PyTorch default.
```

--------------------------------

### Initialize Dataset with getitem_size

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/models/data.html

Initializes the Dataset with a specified fetch size for each __getitem__ call. Sets up internal states for shuffling.

```python
def __init__(self, getitem_size: int = 1) -> None:
        super().__init__()
        self.getitem_size = getitem_size
        self.shuffle_seed: Optional[int] = None
        self.seed_queue: Optional[multiprocessing.Queue] = None
        self.propose_queue: Optional[multiprocessing.Queue] = None
        self.propose_cache: Mapping[int, Any] = {}
```

--------------------------------

### Get Bedtools Path

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/utils.html

Retrieves the path to the bedtools executable. The default value is 'bedtools'.

```python
def BEDTOOLS_PATH(self) -> str:
        r"""
        Path to bedtools executable.
        Default value is ``bedtools``.
        """
        return self._BEDTOOLS_PATH
```

--------------------------------

### Class: scglue.models.plugins.EarlyStopping

Source: https://scglue.readthedocs.io/en/latest/api/scglue.models.plugins.EarlyStopping.html

Initializes the EarlyStopping plugin to monitor training loss and trigger early termination.

```APIDOC
## Class: scglue.models.plugins.EarlyStopping

### Description
Early stop model training when loss no longer decreases.

### Parameters
- **monitor** (str) - Required - Loss to monitor
- **patience** (int) - Required - Patience to stop early
- **burnin** (int) - Optional - Burn-in epochs to skip before initializing early stopping (default: 0)
- **wait_n_lrs** (int) - Optional - Wait n learning rate scheduling events before starting early stopping (default: 0)

### Methods
- **attach**: Attach custom handlers to training or validation engine
```

--------------------------------

### GLUE Device Management

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/models/glue.html

Property to get or set the device (CPU/GPU) for the GLUE module.

```APIDOC
## GLUE.device

### Description
Manages the device assignment for the GLUE module. Setting this property automatically moves the module to the specified device.

### Parameters
- **device** (torch.device) - Required - The target device for the module.
```

--------------------------------

### SCGLUETrainer Initialization and Properties

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/models/scglue.html

Details the initialization parameters for the SCGLUETrainer and its properties like `freeze_u`.

```APIDOC
## SCGLUETrainer

### Description
Trainer for :class:`SCGLUE`.

### Parameters
- **net** (:class:`SCGLUE`) - The SCGLUE network to be trained.
- **lam_data** (float) - Data weight.
- **lam_kl** (float) - KL weight.
- **lam_graph** (float) - Graph weight.
- **lam_align** (float) - Adversarial alignment weight.
- **lam_sup** (float) - Cell type supervision weight.
- **dsc_steps** (int) - Number of discriminator steps per encoder-decoder step.
- **normalize_u** (bool) - Whether to L2 normalize cell embeddings before decoder.
- **modality_weight** (Mapping[str, float]) - Relative modality weight (indexed by modality name).
- **optim** (str) - Optimizer.
- **lr** (float) - Learning rate.
- **kwargs** - Additional keyword arguments passed to the optimizer constructor.

### Properties
#### freeze_u
- **Description**: Whether to freeze cell embeddings.
- **Setter**: Sets the `_freeze_u` attribute and controls `requires_grad` for relevant parameters.

### Initialization Details
- **BURNIN_NOISE_EXAG**: float = 1.5
- Raises `ValueError` if `lam_sup` or `normalize_u` are not specified.
- Initializes `vae_optim` if `net.u2c` is present.
```

--------------------------------

### Model Training with scglue

Source: https://scglue.readthedocs.io/en/latest/_sources/reginf.ipynb.txt

Shows how to train a scglue model. This involves preparing the data and specifying training parameters.

```python
model = scglue.genomics.load_model("model.d/')"
scglue.train.train_scanpy_model(adata, model, "cell_type", "batch")
```

--------------------------------

### Fit Network with Training Parameters

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/models/glue.html

Configures and initiates the network training process. It accepts various parameters to control data loading, batch sizes, training duration, and optimization.

```python
    def fit(
        self,
        data: ArrayDataset,
        graph: GraphDataset,
        val_split: float = None,
        data_batch_size: int = None,
        graph_batch_size: int = None,
        align_burnin: int = None,
        safe_burnin: bool = True,
        max_epochs: int = None,
        patience: Optional[int] = None,
        reduce_lr_patience: Optional[int] = None,
        wait_n_lrs: Optional[int] = None,
        random_seed: int = None,
        directory: Optional[os.PathLike] = None,
        plugins: Optional[List[TrainingPlugin]] = None,
    ) -> None:
        r"""
        Fit network

        Parameters
        ----------
        data
            Data dataset
        graph
            Graph dataset
        val_split
            Validation split
        data_batch_size
            Number of samples in each data minibatch
        graph_batch_size
            Number of edges in each graph minibatch
        align_burnin
            Number of epochs to wait before starting alignment
        safe_burnin
            Whether to postpone learning rate scheduling and earlystopping
            until after the burnin stage
        max_epochs
            Maximal number of epochs
        patience
            Patience of early stopping
        reduce_lr_patience
            Patience to reduce learning rate
        wait_n_lrs
            Wait n learning rate scheduling events before starting early stopping
        random_seed
            Random seed
        directory
            Directory to store checkpoints and tensorboard logs
        plugins
            Optional list of training plugins
        """
        required_kwargs = (
            "val_split",
            "data_batch_size",
            "graph_batch_size",
            "align_burnin",
            "max_epochs",

```

--------------------------------

### Looping Through Data

Source: https://scglue.readthedocs.io/en/latest/_sources/reginf.ipynb.txt

Example of iterating through a list or DataFrame rows. Essential for processing collections of data.

```python
for index, row in df.iterrows():
    print(f"Index: {index}, Feature1: {row['feature1']}")
```

--------------------------------

### Get State Dictionary

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/models/glue.html

Returns the state dictionary for the model, including optimizers for VAE and discriminator.

```python
return {
    **super().state_dict(),
    "vae_optim": self.vae_optim.state_dict(),
    "dsc_optim": self.dsc_optim.state_dict(),
}
```

--------------------------------

### Initialize NBMixtureDataDecoder Parameters

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/models/sc.html

Initializes parameters for the Mixture of Negative Binomial data decoder. Includes scale, two biases, log_theta, and zi_logits.

```python
self.scale_lin = torch.nn.Parameter(torch.zeros(n_batches, out_features))
        self.bias1 = torch.nn.Parameter(torch.zeros(n_batches, out_features))
        self.bias2 = torch.nn.Parameter(torch.zeros(n_batches, out_features))
        self.log_theta = torch.nn.Parameter(torch.zeros(n_batches, out_features))
        self.zi_logits = torch.nn.Parameter(torch.zeros(n_batches, out_features))
```

--------------------------------

### Model.compile Method

Source: https://scglue.readthedocs.io/en/latest/api/scglue.models.base.Model.compile.html

Prepares the model for training by initializing and configuring a trainer. Accepts trainer type and arguments for the trainer constructor.

```APIDOC
## Model.compile

### Description
Prepare model for training.

### Method
(Implicitly called, not a direct HTTP method)

### Endpoint
(N/A - Python method)

### Parameters
#### Path Parameters
(N/A)

#### Query Parameters
(N/A)

#### Request Body
(N/A)

### Request Example
(N/A - Python method)

### Response
#### Success Response (200)
`None`

#### Response Example
(N/A - Python method returns None)

### Parameters
- **trainer** (type) - Required - Trainer type.
- ***args** (type) - Required - Positional arguments are passed to the trainer constructor.
- ****kwargs** (type) - Required - Keyword arguments are passed to the trainer constructor.

### Return type
`None`

### Note
Subclasses may override arguments for API definition.
```

--------------------------------

### Get Model Losses

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/models/base.html

An alias for `.trainer.get_losses`. This method retrieves the loss values calculated by the trainer.

```python
self.trainer.get_losses(*args, **kwargs)
```

--------------------------------

### Initialize BetaBinomial Distribution

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/models/prob.html

Initializes the distribution using logit-transformed mean and concentration parameters. Requires torch.Tensor inputs.

```python
def __init__(self, logit_mu: torch.Tensor, size: torch.Tensor) -> None:
    mu = logit_mu.sigmoid()
    super().__init__(mu * size + EPS, (1 - mu) * size + EPS)
```

--------------------------------

### Initialize BetaDataDecoder Parameters

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/models/sc.html

Initializes parameters for the Beta data decoder. Includes scale, bias, and size parameters, with size clamped to a maximum value.

```python
self.scale_lin = torch.nn.Parameter(torch.zeros(n_batches, out_features))
        self.bias = torch.nn.Parameter(torch.zeros(n_batches, out_features))
        self.size_lin = torch.nn.Parameter(torch.zeros(n_batches, out_features))
```

--------------------------------

### SCGLUE Model Fine-tuning

Source: https://scglue.readthedocs.io/en/latest/training.html

This log snippet shows the initiation of SCGLUE model fine-tuning after pretraining. It includes steps like estimating balancing weights, clustering cells, and matching clusters.

```log
[INFO] fit_SCGLUE: Estimating balancing weight...
[INFO] estimate_balancing_weight: Clustering cells...
[INFO] estimate_balancing_weight: Matching clusters...
[INFO] estimate_balancing_weight: Matching array shape = (16, 18)...
[INFO] estimate_balancing_weight: Estimating balancing weight...
[INFO] fit_SCGLUE: Fine-tuning SCGLUE model...
[INFO] check_graph: Checking variable coverage...
[INFO] check_graph: Checking edge attributes...
[INFO] check_graph: Checking self-loops...
[INFO] check_graph: Checking graph symmetry...
[INFO] SCGLUEModel: Setting `graph_batch_size` = 27025
[INFO] SCGLUEModel: Setting `align_burnin` = 31
[INFO] SCGLUEModel: Setting `max_epochs` = 186

```

--------------------------------

### GET scglue.models.nn.autodevice

Source: https://scglue.readthedocs.io/en/latest/api/scglue.models.nn.autodevice.html

Retrieves the optimal torch computation device based on GPU availability and memory usage.

```APIDOC
## GET scglue.models.nn.autodevice

### Description
Get torch computation device automatically based on GPU availability and memory usage.

### Method
GET

### Endpoint
scglue.models.nn.autodevice()

### Response
#### Success Response (200)
- **device** (torch.device) - The computation device (CPU or GPU) selected based on availability and memory usage.
```

--------------------------------

### Initialize PairedSCGLUETrainer

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/models/scglue.html

Initializes the PairedSCGLUETrainer with network, weighting parameters, and optimizer settings. Requires `lam_joint_cross`, `lam_real_cross`, and `lam_cos` to be specified.

```python
def __init__(
        self,
        net: SCGLUE,
        lam_data: float = None,
        lam_kl: float = None,
        lam_graph: float = None,
        lam_align: float = None,
        lam_sup: float = None,
        lam_joint_cross: float = None,
        lam_real_cross: float = None,
        lam_cos: float = None,
        dsc_steps: int = None,
        normalize_u: bool = None,
        modality_weight: Mapping[str, float] = None,
        optim: str = None,
        lr: float = None,
        **kwargs,
    ) -> None:
        super().__init__(
            net,
            lam_data=lam_data,
            lam_kl=lam_kl,
            lam_graph=lam_graph,
            lam_align=lam_align,
            lam_sup=lam_sup,
            dsc_steps=dsc_steps,
            normalize_u=normalize_u,
            modality_weight=modality_weight,
            optim=optim,
            lr=lr,
            **kwargs,
        )
        required_kwargs = ("lam_joint_cross", "lam_real_cross", "lam_cos")
        for required_kwarg in required_kwargs:
            if locals()[required_kwarg] is None:
                raise ValueError(f"`{required_kwarg}` must be specified!")
        self.lam_joint_cross = lam_joint_cross
        self.lam_real_cross = lam_real_cross
        self.lam_cos = lam_cos
        self.required_losses += ["joint_cross_loss", "real_cross_loss", "cos_loss"]
```

--------------------------------

### Beta Class Initialization

Source: https://scglue.readthedocs.io/en/latest/api/scglue.models.prob.Beta.html

Initializes the Beta distribution with logit of mean and concentration.

```APIDOC
## Beta Class

### Description
Stable beta distribution parameterized by mean and concentration.

### Method
__init__

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
- **logit_mu** (torch.Tensor) - Required - Logit of mean of the beta distribution
- **size** (torch.Tensor) - Required - Concentration of the beta distribution

### Request Example
```python
# Example usage (assuming torch is imported as T)
# logit_mu_tensor = T.tensor([...])
# size_tensor = T.tensor([...])
# beta_distribution = scglue.models.prob.Beta(logit_mu_tensor, size_tensor)
```

### Response
#### Success Response (200)
None (Initialization does not return a value)

#### Response Example
None
```

--------------------------------

### Get Checkpoint Save Numbers

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/utils.html

Retrieves the maximum number of checkpoints to preserve. The default value is 3.

```python
def CHECKPOINT_SAVE_NUMBERS(self) -> int:
        r"""
        Maximal number of checkpoints to preserve at any point.
        Default value is ``3``.
        """
        return self._CHECKPOINT_SAVE_NUMBERS
```

--------------------------------

### SCCLUETrainer Initialization

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/models/scclue.html

Initializes the SCCLUETrainer with the provided network architecture and training hyperparameters.

```APIDOC
## SCCLUETrainer.__init__

### Description
Initializes the trainer instance with required loss weights, optimization settings, and network parameters.

### Parameters
- **net** (SCCLUE) - Required - The SCGLUE network model.
- **lam_data** (float) - Required - Weight for data loss.
- **lam_kl** (float) - Required - Weight for KL divergence loss.
- **lam_align** (float) - Required - Weight for alignment loss.
- **lam_sup** (float) - Required - Weight for supervised loss.
- **lam_joint_cross** (float) - Required - Weight for joint cross loss.
- **lam_real_cross** (float) - Required - Weight for real cross loss.
- **lam_cos** (float) - Required - Weight for cosine loss.
- **normalize_u** (bool) - Required - Whether to normalize latent representations.
- **modality_weight** (Mapping[str, float]) - Required - Weights for different modalities.
- **optim** (str) - Required - Name of the optimizer to use.
- **lr** (float) - Required - Learning rate.
```

--------------------------------

### scglue.check.check_deps

Source: https://scglue.readthedocs.io/en/latest/api/scglue.check.check_deps.html

Checks if specified dependencies are installed. This function takes a variable number of arguments, each representing a dependency to check.

```APIDOC
## scglue.check.check_deps

### Description
Checks whether certain dependencies are installed.

### Method
N/A (This is a Python function, not a REST API endpoint)

### Endpoint
N/A

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Request Example
```python
import scglue

# Example: Check for 'numpy' and 'pandas'
scglue.check.check_deps('numpy', 'pandas')
```

### Response
#### Success Response (None)
This function does not return any value (`None`). It may raise an error if dependencies are not met, but this is not explicitly documented.

#### Response Example
None
```

--------------------------------

### SCCLUETrainer Initialization

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/models/scclue.html

Initializes the SCCLUETrainer with various lambda values for different loss components, network parameters, and optimizer settings. It validates required keyword arguments and sets up optimizers for VAE and discriminator components.

```python
class SCCLUETrainer(Trainer):
    def __init__(
        self,
        net: SCCLUE,
        lam_data: float = None,
        lam_kl: float = None,
        lam_align: float = None,
        lam_sup: float = None,
        lam_joint_cross: float = None,
        lam_real_cross: float = None,
        lam_cos: float = None,
        normalize_u: bool = None,
        modality_weight: Mapping[str, float] = None,
        optim: str = None,
        lr: float = None,
        **kwargs,
    ) -> None:
        required_kwargs = (
            "lam_data",
            "lam_kl",
            "lam_align",
            "lam_sup",
            "lam_joint_cross",
            "lam_real_cross",
            "lam_cos",
            "normalize_u",
            "modality_weight",
            "optim",
            "lr",
        )
        for required_kwarg in required_kwargs:
            if locals()[required_kwarg] is None:
                raise ValueError(f"`{required_kwarg}` must be specified!")

        super().__init__(net)
        self.required_losses = [
            "dsc_loss",
            "gen_loss",
            "joint_cross_loss",
            "real_cross_loss",
            "cos_loss",
        ]
        if self.net.u2c:
            self.required_losses.append("sup_loss")
        for k in self.net.keys:
            self.required_losses += [f"x_{k}_nll", f"x_{k}_kl", f"x_{k}_elbo"]
        self.earlystop_loss = "gen_loss"

        self.lam_data = lam_data
        self.lam_kl = lam_kl
        self.lam_align = lam_align
        self.lam_sup = lam_sup
        self.lam_joint_cross = lam_joint_cross
        self.lam_real_cross = lam_real_cross
        self.lam_cos = lam_cos
        self.normalize_u = normalize_u
        if min(modality_weight.values()) < 0:
            raise ValueError("Modality weights must be non-negative!")
        normalizer = sum(modality_weight.values()) / len(modality_weight)
        self.modality_weight = {k: v / normalizer for k, v in modality_weight.items()}

        self.lr = lr
        if self.net.u2c:
            self.vae_optim = getattr(torch.optim, optim)(
                chain(
                    self.net.x2u.parameters(),
                    self.net.u2x.parameters(),
                    self.net.u2c.parameters(),
                ),
                lr=self.lr,
                **kwargs,
            )
        else:
            self.vae_optim = getattr(torch.optim, optim)(
                chain(self.net.x2u.parameters(), self.net.u2x.parameters()),
                lr=self.lr,
                **kwargs,
            )
        self.dsc_optim = getattr(torch.optim, optim)(
            self.net.du.parameters(), lr=self.lr, **kwargs
        )

        self.align_burnin: Optional[int] = None
```

--------------------------------

### Gene Expression Analysis

Source: https://scglue.readthedocs.io/en/latest/_sources/reginf.ipynb.txt

Example of analyzing gene expression patterns. This snippet focuses on plotting specific genes.

```python
sc.pl.umap(adata, color=["gene1", "gene2"])
```

--------------------------------

### Execute Model Training

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/models/glue.html

Initiates the model training process using the configured data loaders and plugins. Includes a try-finally block to ensure data cleanup after training completes or if an error occurs. This is the main entry point for training.

```python
super().fit(
                train_loader,
                val_loader=val_loader,
                max_epochs=max_epochs,
                random_seed=random_seed,
                directory=directory,
                plugins=plugins,
            )
        finally:
            data.clean()
            data_train.clean()
            data_val.clean()
            graph.clean()
            self.align_burnin = None
            self.eidx = None
            self.enorm = None
            self.esgn = None
```

--------------------------------

### Model Compilation

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/models/base.html

Explains how to prepare the model for training by compiling it with a trainer.

```APIDOC
## compile

### Description
Prepares the model for training by initializing and assigning a trainer.

### Parameters
- **trainer** (Trainer) - The trainer type to be used.
- **args** - Positional arguments passed to the trainer constructor.
- **kwargs** - Keyword arguments passed to the trainer constructor.

### Note
Subclasses may override arguments for API definition. Calling `compile` multiple times will overwrite the previous trainer.
```

--------------------------------

### Data Filtering

Source: https://scglue.readthedocs.io/en/latest/_sources/reginf.ipynb.txt

Example of filtering a DataFrame based on specific conditions. This is crucial for selecting relevant data subsets.

```python
filtered_df = df[df['feature1'] > 0.7]
print(filtered_df.head())
```

--------------------------------

### Compile Model for Training

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/models/base.html

Prepares the model for training by constructing the trainer. It can overwrite an existing trainer if called multiple times.

```python
if self._trainer:
            self.logger.warning(
                "`compile` has already been called. "
                "Previous trainer will be overwritten!"
            )
        self._trainer = self.TRAINER_TYPE(self.net, *args, **kwargs)
```

--------------------------------

### Initialize AllCoolsLSI class

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/data.html

Initializes the AllCoolsLSI transformer with specified parameters for dimensionality reduction.

```python
class AllCoolsLSI:
    def __init__(
        self,
        scale_factor=100000,
        n_components=100,
        algorithm="arpack",
        random_state=0,
        n_iter=5,
        idf=None,
        model=None,
    ):
        self.scale_factor = scale_factor
        if idf is not None:
            self.idf = idf.copy()
        else:
            self.idf = None
        if model is not None:
            self.model = model
        else:
            self.model = TruncatedSVD(
                n_components=n_components,
                n_iter=n_iter,
                algorithm=algorithm,
                random_state=random_state,
            )
        self.random_state = random_state
        self.fitted = False

    def _downsample_data(self, data, downsample):
        np.random.seed(self.random_state)
        if downsample is not None and downsample < data.shape[0]:
            use_row_idx = np.sort(
```

--------------------------------

### Get Print Loss Interval

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/utils.html

Retrieves the interval in epochs for printing loss values. The default value is 10.

```python
def PRINT_LOSS_INTERVAL(self) -> int:
        r"""
        Print loss values every n epochs.
        Default value is ``10``.
        """
        return self._PRINT_LOSS_INTERVAL
```

--------------------------------

### Basic Data Loading and Processing

Source: https://scglue.readthedocs.io/en/latest/_sources/reginf.ipynb.txt

Demonstrates loading and basic processing of data. Ensure necessary libraries are imported before use.

```python
import scanpy as sc
import scglue

adata = sc.read_h5ad("adata.h5ad")
adata.var_names_make_unique()

scglue.data.get_common_counts(adata, "batch", "cell_type")
```

--------------------------------

### Get Checkpoint Save Interval

Source: https://scglue.readthedocs.io/en/latest/_modules/scglue/utils.html

Retrieves the interval in epochs for automatically saving checkpoints. The default value is 10.

```python
def CHECKPOINT_SAVE_INTERVAL(self) -> int:
        r"""
        Automatically save checkpoints every n epochs.
        Default value is ``10``.
        """
        return self._CHECKPOINT_SAVE_INTERVAL
```

--------------------------------

### SCGLUEModel Initialization

Source: https://scglue.readthedocs.io/en/latest/api/scglue.models.scglue.SCGLUEModel.html

Constructor for initializing the SCGLUEModel with datasets and guidance graph vertices.

```APIDOC
## SCGLUEModel Constructor

### Description
Initializes the GLUE model for single-cell multi-omics data integration.

### Parameters
- **adatas** (Mapping[str, AnnData]) - Required - Datasets indexed by modality name.
- **vertices** (List[str]) - Required - Guidance graph vertices covering feature names in all modalities.
- **latent_dim** (int) - Optional - Latent dimensionality (default: 50).
- **h_depth** (int) - Optional - Hidden layer depth for encoder and discriminator (default: 2).
- **h_dim** (int) - Optional - Hidden layer dimensionality for encoder and discriminator (default: 256).
- **dropout** (float) - Optional - Dropout rate (default: 0.2).
- **disc_norm** (bool) - Optional - Whether to perform normalization at discriminator input (default: False).
- **shared_batches** (bool) - Optional - Whether the same batches are shared across modalities (default: False).
- **random_seed** (int) - Optional - Random seed (default: 0).
```