class_docstr: Class to extract persistence diagrams from time series.
  
  Args:
      takens_embedding_dim: Dimension of the Takens embedding.
      takens_embedding_delay: Delay of the Takens embedding.
      homology_dimensions: Homology dimensions to compute.
      filtering: Whether to filter the persistence diagrams.
      filtering_dimensions: Homology dimensions to filter.
      parallel: Whether to parallelize the computation.
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code: def transform(self, x_embeddings):
      vr = VietorisRipsPersistence(
          metric="euclidean",
          homology_dimensions=self.homology_dimensions_,
          n_jobs=1,
      )
      diagram_scaler = Scaler(n_jobs=1)
      persistence_diagrams = diagram_scaler.fit_transform(
          vr.fit_transform([x_embeddings])
      )
      if self.filtering_:
          diagram_filter = Filtering(
              epsilon=0.1,
              homology_dimensions=self.filtering_dimensions_,
          )
          persistence_diagrams = diagram_filter.fit_transform(
              persistence_diagrams
          )
      return persistence_diagrams[0]
  
imports: from abc import ABC
  import numpy
  import pandas
  from gtda.diagrams import Scaler, Filtering, PersistenceEntropy, PersistenceLandscape, BettiCurve
  from gtda.homology import VietorisRipsPersistence
label_desc: Use image node_id 2 for calling the PersistenceDiagramsExtractor obj's underlying member method code with example usage: obj.transform(x_embeddings) and returns: persistence_diagrams