>>> import torch
>>> import torch.nn.functional as F
>>> from symile import Symile
>>> 
>>> # Create random embeddings (batch_size=32, embedding_dim=256)
>>> embeddings_a = torch.rand(32, 256)
>>> embeddings_b = torch.rand(32, 256)
>>> 
>>> # Normalize embeddings
>>> embeddings_a = F.normalize(embeddings_a, p=2.0, dim=1)
>>> embeddings_b = F.normalize(embeddings_b, p=2.0, dim=1)
>>> 
>>> # Temperature parameter
>>> temperature = 0.07
>>> 
>>> # Compute InfoNCE loss
>>> logits = embeddings_a @ embeddings_b.T / temperature
>>> labels = torch.arange(embeddings_a.shape[0])
>>> infonce_loss = F.cross_entropy(logits, labels)
>>> 
>>> # Compute Symile loss
>>> symile_loss = Symile()([embeddings_a, embeddings_b], temperature)
>>> 
>>> print(f"InfoNCE Loss: {infonce_loss.item():.4f}")
InfoNCE Loss: 3.5327
>>> print(f"Symile Loss: {symile_loss.item():.4f}")
Symile Loss: 3.4659