input: Write a python function to perform PCA on a given dataset and return the explained variance ratio of each principal component.
  assert pca_explained_variance_ratio([[1, 2], [3, 4], [5, 6], [7, 8]]) == [0.99244332, 0.00755668]
  assert pca_explained_variance_ratio([[2, 0, 0], [0, 2, 0], [0, 0, 3]]) == [0.66666667, 0.33333333, 0.]
  assert pca_explained_variance_ratio([[1, 1], [2, 2], [3, 3]]) == [1., 0.]
output: def pca_explained_variance_ratio(data):
      from sklearn.decomposition import PCA
      import numpy as np
      
      pca = PCA()
      pca.fit(data)
      explained_variance_ratio = pca.explained_variance_ratio_
      
      return explained_variance_ratio.tolist()