class_docstr: This is a contextual dataset that is constructed from either a single
  dateset containing overall outcome or a list of datasets that each corresponds
  to a context breakdown.
docstr: Validation of given datasets.
  1. each dataset has same X.
  2. metric_decomposition is not None if there are multiple datasets.
  3. metric_decomposition contains all the outcomes in datasets.
  4. value keys of parameter decomposition and the keys of
  metric_decomposition match context buckets.
  5. Yvar is None for all, or not for all.
code: def _validate_datasets(
      self,
      datasets: List[SupervisedDataset],
      metric_decomposition: Optional[Dict[str, List[str]]] = None,
  ) -> None:
      """Validation of given datasets.
      1. each dataset has same X.
      2. metric_decomposition is not None if there are multiple datasets.
      3. metric_decomposition contains all the outcomes in datasets.
      4. value keys of parameter decomposition and the keys of
      metric_decomposition match context buckets.
      5. Yvar is None for all, or not for all.
      """
      X = datasets[0].X
      Yvar_is_none = datasets[0].Yvar is None
      for dataset in datasets:
          if torch.equal(X, dataset.X) is not True:
              raise InputDataError("Require same X for context buckets")
          if (dataset.Yvar is None) != Yvar_is_none:
              raise InputDataError(
                  "Require Yvar to be specified for all buckets, or for none"
              )
  
      if len(datasets) > 1:
          if metric_decomposition is None:
              raise InputDataError(
                  "metric_decomposition must be provided when there are"
                  + " multiple datasets."
              )
      else:
          if metric_decomposition is not None:
              raise InputDataError(
                  "metric_decomposition is redundant when there is one "
                  + "dataset for overall outcome."
              )
  
      if collections.Counter(
          list(self.parameter_decomposition.keys())
      ) != collections.Counter(self.context_buckets):
          raise InputDataError(
              "Keys of parameter decomposition and context buckets do not match."
          )
  
      if metric_decomposition is not None:
          if collections.Counter(
              list(self.metric_decomposition.keys())
          ) != collections.Counter(self.context_buckets):
              raise InputDataError(
                  "Keys of metric decomposition and context buckets do not match."
              )
  
          all_metrics = []
          for m in metric_decomposition.values():
              all_metrics.extend(m)
          for outcome in self.outcome_names:
              if outcome not in all_metrics:
                  raise InputDataError(
                      f"{outcome} is missing in metric_decomposition."
                  )
  
imports: from __future__ import annotations
  import collections
  import warnings
  from typing import Any, Dict, List, Optional, Union
  import torch
  from botorch.exceptions.errors import InputDataError, UnsupportedError
  from botorch.utils.containers import BotorchContainer, SliceContainer
  from torch import long, ones, Tensor
label_desc: Use image node_id 6 for calling the ContextualDataset obj's underlying member method code with example usage: obj._validate_datasets(datasets, metric_decomposition) without return types