docstr: Explains the instances in `X`. `y` should be passed if the model loss function is to be explained,
  which can be useful in order to understand how various features affect model performance over
  time. This is only possible if the explainer has been fitted with a background dataset and
  requires setting `model_output='log_loss'`.
  
  Parameters
  ----------
  X
      Instances to be explained.
  y
      Labels corresponding to rows of `X`. Should be passed only if a background dataset was passed to the
      `fit` method.
  interactions
      If ``True``, the shap value for every feature of every instance in `X` is decomposed into
      `X.shape[1] - 1` shap value interactions and one main effect. This is only supported if `fit` is called
      with `background_dataset=None`.
  approximate
      If ``True``, an approximation to the shap values that does not account for feature order is computed. This
      was proposed by `Ando Sabaas`_ here . Check `this`_ resource for more details. This option is currently
      only supported for `xgboost` and `sklearn` models.
  
      .. _Ando Sabaas:
         https://github.com/andosa/treeinterpreter
  
      .. _this:
         https://static-content.springer.com/esm/art%3A10.1038%2Fs42256-019-0138-9/MediaObjects/42256_2019_138_MOESM1_ESM.pdf
  
  check_additivity
      If ``True``, output correctness is ensured if ``model_output='raw'`` has been passed to the constructor.
  tree_limit
      Explain the output of a subset of the first `tree_limit` trees in an ensemble model.
  summarise_result
      This should be set to ``True`` only when some of the columns in `X` represent encoded dimensions of a
      categorical variable and one single shap value per categorical variable is desired. Both
      `cat_vars_start_idx` and `cat_vars_enc_dim` should be specified as detailed below to allow this.
  cat_vars_start_idx
      The start indices of the categorical variables.
  cat_vars_enc_dim
      The length of the encoding dimension for each categorical variable.
  
  Returns
  -------
  explanation
      An `Explanation` object containing the shap values and prediction in the `data` field, along with a
      `meta` field containing additional data. See usage at `TreeSHAP examples`_ for details.
  
      .. _TreeSHAP examples:
         https://docs.seldon.io/projects/alibi/en/stable/methods/TreeSHAP.html
code: def explain(
      self,
      X: Union[np.ndarray, pd.DataFrame, "catboost.Pool"],
      y: Optional[np.ndarray] = None,
      interactions: bool = False,
      approximate: bool = False,
      check_additivity: bool = True,
      tree_limit: Optional[int] = None,
      summarise_result: bool = False,
      cat_vars_start_idx: Optional[Sequence[int]] = None,
      cat_vars_enc_dim: Optional[Sequence[int]] = None,
      **kwargs,
  ) -> "Explanation":
      """
      Explains the instances in `X`. `y` should be passed if the model loss function is to be explained,
      which can be useful in order to understand how various features affect model performance over
      time. This is only possible if the explainer has been fitted with a background dataset and
      requires setting `model_output='log_loss'`.
  
      Parameters
      ----------
      X
          Instances to be explained.
      y
          Labels corresponding to rows of `X`. Should be passed only if a background dataset was passed to the
          `fit` method.
      interactions
          If ``True``, the shap value for every feature of every instance in `X` is decomposed into
          `X.shape[1] - 1` shap value interactions and one main effect. This is only supported if `fit` is called
          with `background_dataset=None`.
      approximate
          If ``True``, an approximation to the shap values that does not account for feature order is computed. This
          was proposed by `Ando Sabaas`_ here . Check `this`_ resource for more details. This option is currently
          only supported for `xgboost` and `sklearn` models.
  
          .. _Ando Sabaas:
             https://github.com/andosa/treeinterpreter
  
          .. _this:
             https://static-content.springer.com/esm/art%3A10.1038%2Fs42256-019-0138-9/MediaObjects/42256_2019_138_MOESM1_ESM.pdf
  
      check_additivity
          If ``True``, output correctness is ensured if ``model_output='raw'`` has been passed to the constructor.
      tree_limit
          Explain the output of a subset of the first `tree_limit` trees in an ensemble model.
      summarise_result
          This should be set to ``True`` only when some of the columns in `X` represent encoded dimensions of a
          categorical variable and one single shap value per categorical variable is desired. Both
          `cat_vars_start_idx` and `cat_vars_enc_dim` should be specified as detailed below to allow this.
      cat_vars_start_idx
          The start indices of the categorical variables.
      cat_vars_enc_dim
          The length of the encoding dimension for each categorical variable.
  
      Returns
      -------
      explanation
          An `Explanation` object containing the shap values and prediction in the `data` field, along with a
          `meta` field containing additional data. See usage at `TreeSHAP examples`_ for details.
  
          .. _TreeSHAP examples:
             https://docs.seldon.io/projects/alibi/en/stable/methods/TreeSHAP.html
  
      """  # noqa: E501
  
      if not self._fitted:
          raise TypeError(
              "Called explain on an unfitted object! Please fit the "
              "explainer using the .fit method first!"
          )
  
      self.tree_limit = tree_limit
      self.approximate = approximate
      self.interactions = interactions
      self.summarise_result = summarise_result
  
      if interactions:
          self._check_interactions(approximate, self.background_data, y)
          if self._explainer.model.model_type == "xgboost":
              shap_output = self._xgboost_interactions(X)
          else:
              shap_output = self._explainer.shap_interaction_values(
                  X, tree_limit=tree_limit
              )
      else:
          self._check_explainer_setup(
              self.background_data, self.model_output, y
          )
          shap_output = self._explainer.shap_values(
              X,
              y=y,
              tree_limit=tree_limit,
              approximate=self.approximate,
              check_additivity=check_additivity,
          )
      self.expected_value = self._explainer.expected_value
      expected_value = self.expected_value
      if isinstance(shap_output, np.ndarray):
          shap_output = [shap_output]
      if isinstance(expected_value, float):
          expected_value = [expected_value]
  
      self._update_metadata(
          {
              "interactions": self.interactions,
              "explain_loss": True if y is not None else False,
              "approximate": self.approximate,
          },
          params=True,
      )
  
      explanation = self._build_explanation(
          X,
          shap_output,
          expected_value,
          y=y,
          summarise_result=summarise_result,
          cat_vars_start_idx=cat_vars_start_idx,
          cat_vars_enc_dim=cat_vars_enc_dim,
      )
  
      return explanation
  
imports: import copy
  import logging
  from functools import partial
  from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
  import numpy
  import pandas
  import shap
  import shap.utils._legacy
  from scipy import sparse
  from scipy.special import expit
  from shap import KernelExplainer
  from alibi.api.defaults import DEFAULT_DATA_KERNEL_SHAP, DEFAULT_DATA_TREE_SHAP, DEFAULT_META_KERNEL_SHAP, DEFAULT_META_TREE_SHAP
  from alibi.api.interfaces import Explainer, Explanation, FitMixin
  from alibi.utils.wrappers import methdispatch
label_desc: Use image node_id 6 for calling the TreeShap obj's underlying member method code with example usage: obj.explain(X, y, interactions, approximate, check_additivity, tree_limit, summarise_result, cat_vars_start_idx, cat_vars_enc_dim) and returns: explanation