class_docstr: Stores the explanations for a single row of input data.
  
  Contains the feature attributions for each label in the target feature's vocab.
class_docstr_tok: ["Stores","the","explanations","for","a","single","row","of","input","data",".","Contains","the","feature","attributions","for","each","label","in","the","target","feature","'s","vocab","."]
docstr: Convert the explanation to a 2D array of shape (num_labels, num_features).
docstr_tok: ["Convert","the","explanation","to","a","2D","array","of","shape","(","num_labels",",","num_features",")","."]
code: def to_array(self) -> npt.NDArray[np.float64]:
      """Convert the explanation to a 2D array of shape (num_labels, num_features)."""
      return np.array([le.to_array() for le in self.label_explanations])
  
file_path: ludwig/ludwig/explain/explanation.py
imports: from dataclasses import dataclass, field
  from typing import Dict, List, Tuple
  import numpy
  import numpy.typing
  from ludwig.api_annotations import DeveloperAPI, PublicAPI
label_desc: Use image node_id 2 for calling the Explanation obj's underlying member method code with example usage: obj.to_array() and returns: np