### Install LVMAE-pytorch

Source: https://context7.com/lucidrains/lvmae-pytorch/llms.txt

Install the package from PyPI using pip.

```bash
pip install LVMAE-pytorch
```

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

### Core Dependencies for FSQ

Source: https://context7.com/lucidrains/lvmae-pytorch/llms.txt

Core dependencies for the library, including PyTorch and vector-quantize-pytorch for FSQ operations. The FSQ is used for efficient token quantization in the video autoencoder pipeline.

```python
# Core dependencies
# torch>=2.0
# einops>=0.8.0
# vector-quantize-pytorch>=1.20.9

import torch
from torch.nn import Module, ModuleList
import torch.nn.functional as F
from vector_quantize_pytorch import FSQ

# The FSQ (Finite Scalar Quantization) is used for efficient token quantization
# in the video autoencoder pipeline
```

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

### Initialize LVAME Model

Source: https://context7.com/lucidrains/lvmae-pytorch/llms.txt

Initialize the main LVAME class, which extends PyTorch's nn.Module for processing long video sequences with masked autoencoding and FSQ.

```python
import torch
from LVMAE_pytorch import LVAME

# Initialize the LVMAE model
model = LVAME()

# Note: This is a work-in-progress implementation
# Full API with forward pass and training utilities coming soon
```

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