### Install MIMO-pytorch

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

Installation commands for the library via pip or from source.

```bash
pip install MIMO-pytorch
```

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

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

### Integrate MIMO in Training Loop

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

Example demonstrating how to incorporate the MIMO model into a standard PyTorch training workflow with an optimizer and loss function.

```python
import torch
import torch.nn as nn
from torch.optim import Adam
from MIMO_pytorch import MIMO

# Initialize model and optimizer
model = MIMO()
optimizer = Adam(model.parameters(), lr=1e-4)
criterion = nn.MSELoss()

# Training loop example
def train_step(model, data, target, optimizer, criterion):
    model.train()
    optimizer.zero_grad()

    output = model(data)
    loss = criterion(output, target)

    loss.backward()
    optimizer.step()

    return loss.item()

# Example training iteration
batch_size = 8
x = torch.randn(batch_size, 3, 256, 256)
target = torch.randn(batch_size, 3, 256, 256)

loss = train_step(model, x, target, optimizer, criterion)
print(f"Training loss: {loss:.4f}")
```

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

### Initialize and Use MIMO Model

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

Basic usage of the MIMO module, including input tensor creation, forward pass, and GPU device transfer.

```python
import torch
from MIMO_pytorch import MIMO

# Initialize the MIMO model
model = MIMO()

# Create sample input tensor (batch_size, channels, height, width)
# Example: batch of 4 video frames with 3 color channels at 256x256 resolution
x = torch.randn(4, 3, 256, 256)

# Forward pass through the model
output = model(x)

# Output shape matches input (identity pass-through in current implementation)
print(f"Input shape: {x.shape}")   # torch.Size([4, 3, 256, 256])
print(f"Output shape: {output.shape}")  # torch.Size([4, 3, 256, 256])

# Use with GPU acceleration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)
x = x.to(device)
output = model(x)
```

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

### Handle Defaults with default Utility

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

Utility function to return a default value when the primary input is None.

```python
from MIMO_pytorch.MIMO import default

# Return default when value is None
config_value = None
actual_value = default(config_value, 512)
print(actual_value)  # 512

# Return original value when it exists
config_value = 1024
actual_value = default(config_value, 512)
print(actual_value)  # 1024

# Common usage pattern in model initialization
def create_model(hidden_dim=None, num_layers=None):
    hidden_dim = default(hidden_dim, 256)
    num_layers = default(num_layers, 6)
    return {"hidden_dim": hidden_dim, "num_layers": num_layers}

model_config = create_model()  # Uses defaults: {'hidden_dim': 256, 'num_layers': 6}
model_config = create_model(hidden_dim=512)  # {'hidden_dim': 512, 'num_layers': 6}
```

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

### Check Existence with exists Utility

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

Utility function to verify if a value is not None, commonly used for optional parameter handling.

```python
from MIMO_pytorch.MIMO import exists

# Check if values exist
value1 = None
value2 = torch.randn(3, 3)

print(exists(value1))  # False
print(exists(value2))  # True

# Common usage pattern for optional parameters
def process_tensor(x, mask=None):
    if exists(mask):
        x = x * mask
    return x
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.