### Install Value Network

Source: https://context7.com/lucidrains/value-network/llms.txt

Installation commands for the library via pip or from source.

```bash
pip install value-network
```

```bash
git clone https://github.com/lucidrains/value-network
cd value-network
pip install -e .
```

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

### Perform Tensor Operations

Source: https://context7.com/lucidrains/value-network/llms.txt

Example of using einops for tensor manipulation within the context of value networks.

```python
# Core dependencies required by value-network
# torch>=2.5 - PyTorch deep learning framework
# einops>=0.8.2 - Flexible tensor operations
# hl-gauss-pytorch - HL-Gauss implementation for classification-based value learning
# torch-einops-utils>=0.0.20 - Utilities for einops with PyTorch

import torch
import einops

# Example of tensor operations commonly used with value networks
batch_size, seq_len, hidden_dim = 8, 16, 64
x = torch.randn(batch_size, seq_len, hidden_dim)

# Rearrange tensors for different network architectures
x_reshaped = einops.rearrange(x, 'b s h -> b h s')
print(f"Original shape: {x.shape}")  # Output: Original shape: torch.Size([8, 16, 64])
print(f"Reshaped: {x_reshaped.shape}")  # Output: Reshaped: torch.Size([8, 64, 16])
```

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

### Initialize ValueNetwork

Source: https://context7.com/lucidrains/value-network/llms.txt

Instantiate the ValueNetwork class and move it to the appropriate compute device.

```python
import torch
from value_network import ValueNetwork

# Create a value network instance
value_net = ValueNetwork()

# The ValueNetwork extends torch.nn.Module, so it integrates
# seamlessly with PyTorch's training ecosystem
print(f"Module type: {type(value_net)}")
# Output: Module type: <class 'value_network.value_network.ValueNetwork'>

# Check if the model is on the correct device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
value_net = value_net.to(device)
```

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

### Check Value Existence

Source: https://context7.com/lucidrains/value-network/llms.txt

Use the exists helper to verify if a variable is not None, useful for conditional configuration.

```python
from value_network.value_network import exists

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

config_value = 64
print(exists(config_value))  # Output: True

# Useful for conditional logic in model construction
hidden_dim = 128
if exists(hidden_dim):
    print(f"Using hidden dimension: {hidden_dim}")
# Output: Using hidden dimension: 128
```

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

### Set Default Values

Source: https://context7.com/lucidrains/value-network/llms.txt

Use the default helper to provide fallback values for hyperparameters.

```python
from value_network.value_network import default

# Use default values when None is provided
learning_rate = None
actual_lr = default(learning_rate, 0.001)
print(f"Learning rate: {actual_lr}")  # Output: Learning rate: 0.001

# When value exists, use the provided value
batch_size = 32
actual_batch = default(batch_size, 64)
print(f"Batch size: {actual_batch}")  # Output: Batch size: 32

# Common pattern for model hyperparameters
def create_model(hidden_dim=None, num_layers=None):
    hidden_dim = default(hidden_dim, 256)
    num_layers = default(num_layers, 3)
    return f"Model with {hidden_dim} hidden units and {num_layers} layers"

print(create_model())  # Output: Model with 256 hidden units and 3 layers
print(create_model(512, 5))  # Output: Model with 512 hidden units and 5 layers
```

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