import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Euclidean Distance implementation.
    Computes the Euclidean distance between two sets of vectors.
    """
    def __init__(self):
        super(Model, self).__init__()
    
    def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
        """
        Compute Euclidean distance between x and y.

        Args:
            x (torch.Tensor): First set of vectors [batch_size, feature_dim]
            y (torch.Tensor): Second set of vectors [batch_size, feature_dim]

        Returns:
            torch.Tensor: Euclidean distances [batch_size]
        """
        # Compute squared differences
        diff = x - y
        squared_diff = diff * diff
        
        # Sum along feature dimension
        sum_squared = torch.sum(squared_diff, dim=1)
        
        # Take square root
        distances = torch.sqrt(sum_squared)
        
        return distances

batch_size = 256
feature_dim = 512

def get_inputs():
    # Generate two sets of vectors
    x = torch.randn(batch_size, feature_dim)
    y = torch.randn(batch_size, feature_dim)
    return [x, y]

def get_init_inputs():
    return []  # No special initialization inputs needed
