### Install PathNet via pip or source

Source: https://context7.com/lucidrains/pathnet/llms.txt

Commands to install the library from PyPI or from the source repository for development.

```bash
pip install pathnet
```

```bash
git clone https://github.com/lucidrains/pathnet
cd pathnet
pip install -e .[test]
```

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

### Verify PathNet dependencies

Source: https://context7.com/lucidrains/pathnet/llms.txt

Check the installed versions of required libraries.

```python
# Required dependencies
# - Python >= 3.9
# - torch >= 2.4
# - einops >= 0.8.1

# Verify installation
import torch
import einops

print(f"PyTorch version: {torch.__version__}")
print(f"Einops version: {einops.__version__}")
```

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

### Run PathNet test suite

Source: https://context7.com/lucidrains/pathnet/llms.txt

Commands to install test dependencies and execute the test suite using pytest.

```bash
# Install test dependencies
pip install -e .[test]

# Run tests
python -m pytest tests/
```

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

### Initialize Pathnet Model

Source: https://context7.com/lucidrains/pathnet/llms.txt

Instantiate the Pathnet module, which functions as a standard PyTorch nn.Module.

```python
import torch
from pathnet import Pathnet

# Initialize the PathNet model
model = Pathnet()

# The model is a standard PyTorch Module
# and can be used with typical PyTorch training patterns
print(model)
```

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

### Use default utility function

Source: https://context7.com/lucidrains/pathnet/llms.txt

Provide a fallback value if the primary value is None.

```python
from pathnet.pathnet import default

# Use default values for None parameters
config = None
actual_config = default(config, {"layers": 3, "hidden_dim": 256})
print(actual_config)  # Output: {'layers': 3, 'hidden_dim': 256}

# When value exists, it's returned as-is
config = {"layers": 5}
actual_config = default(config, {"layers": 3, "hidden_dim": 256})
print(actual_config)  # Output: {'layers': 5}
```

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

### Utility Functions

Source: https://context7.com/lucidrains/pathnet/llms.txt

Helper functions for handling optional parameters and default values within the PathNet library.

```APIDOC
## exists(v)

### Description
A utility function that checks if a value is not None.

## default(v, d)

### Description
A utility function that returns a value if it exists, otherwise returns a provided default value.
```

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

### Use exists utility function

Source: https://context7.com/lucidrains/pathnet/llms.txt

Check if a variable is not None using the exists helper.

```python
from pathnet.pathnet import exists

# Check if values exist
value = None
print(exists(value))  # Output: False

value = 42
print(exists(value))  # Output: True

value = []
print(exists(value))  # Output: True (empty list is not None)
```

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

### Pathnet Class

Source: https://context7.com/lucidrains/pathnet/llms.txt

The primary neural network module for implementing the PathNet architecture, extending PyTorch's nn.Module.

```APIDOC
## Pathnet Class

### Description
The Pathnet class is the main neural network module that implements the PathNet architecture, allowing for modular neural networks with evolutionary pathway selection.

### Usage
```python
from pathnet import Pathnet
model = Pathnet()
```
```

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