import torch
import torch.nn as nn


class UstaLayerNorm(nn.Module):
  def __init__(self, embedding_dim, eps=1e-5):
    super().__init__()
    self.eps = eps

    self.weight = nn.Parameter(torch.ones(embedding_dim))

    
  def forward(self, x):
    mean = x.mean(dim=-1, keepdim=True)
    variance = x.var(dim=-1, keepdim=True, unbiased=False)
    normalized_x = (x - mean) / torch.sqrt(variance + self.eps)
    return self.weight * normalized_x