class_docstr: Uses Shapley values to explain any machine learning model or python function.
  
  This is the primary explainer interface for the SHAP library. It takes any combination
  of a model and masker and returns a callable subclass object that implements
  the particular estimation algorithm that was chosen.
docstr: Explains a single row and returns the tuple (row_values, row_expected_values, row_mask_shapes, main_effects).
  
  This is an abstract method meant to be implemented by each subclass.
  
  Returns
  -------
  tuple
      A tuple of (row_values, row_expected_values, row_mask_shapes), where row_values is an array of the
      attribution values for each sample, row_expected_values is an array (or single value) representing
      the expected value of the model for each sample (which is the same for all samples unless there
      are fixed inputs present, like labels when explaining the loss), and row_mask_shapes is a list
      of all the input shapes (since the row_values is always flattened),
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code: def explain_row(
      self,
      *row_args,
      max_evals,
      main_effects,
      error_bounds,
      outputs,
      silent,
      **kwargs,
  ):
      """Explains a single row and returns the tuple (row_values, row_expected_values, row_mask_shapes, main_effects).
  
      This is an abstract method meant to be implemented by each subclass.
  
      Returns
      -------
      tuple
          A tuple of (row_values, row_expected_values, row_mask_shapes), where row_values is an array of the
          attribution values for each sample, row_expected_values is an array (or single value) representing
          the expected value of the model for each sample (which is the same for all samples unless there
          are fixed inputs present, like labels when explaining the loss), and row_mask_shapes is a list
          of all the input shapes (since the row_values is always flattened),
      """
  
      return {}
  
imports: import copy
  import time
  import numpy
  import pandas
  import scipy.sparse
  from ..None import explainers, links, maskers, models
  from .._explanation import Explanation
  from .._serializable import Deserializer, Serializable, Serializer
  from ..maskers import Masker
  from ..models import Model
  from ..utils import safe_isinstance, show_progress
  from ..utils._exceptions import InvalidAlgorithmError
  from ..utils.transformers import is_transformers_lm
label_desc: Use image node_id 3 for calling the Explainer obj's underlying member method code with example usage: obj.explain_row() and returns: dict