code: def lle_feat(df, drop=None, components=4):
      if drop:
          keep = df[drop]
          df = df.drop(drop, axis=1)
  
      embedding = LocallyLinearEmbedding(n_components=components)
      em = embedding.fit_transform(df)
      df = pd.DataFrame(em, index=df.index)
      df = df.add_prefix("lle_")
      if drop:
          df = pd.concat((keep, df), axis=1)
      return df
  
file_path: deltapy/deltapy/mapper.py
imports: from sklearn.decomposition import PCA, IncrementalPCA, KernelPCA
  from sklearn.kernel_approximation import AdditiveChi2Sampler
  from sklearn.cross_decomposition import CCA
  from sklearn.manifold import LocallyLinearEmbedding
  from sklearn.preprocessing import minmax_scale
  from sklearn import datasets, cluster
  from sklearn.neighbors import NearestNeighbors
  import numpy
  import pandas
  import tensorflow
label_desc: Use image node_id 5 for calling a global function with example usage: lle_feat(df, drop, components) and returns: df