class_docstr: Imputed Data
  
  This class should not be instantiated directly.
  Instead, it is returned when ImputationKernel.impute_new_data() is called.
  For parameter arguments, see ImputationKernel documentation.
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docstr: Plot the correlations between datasets.
  See get_correlations() for more details.
  
  Parameters
  ----------
  datasets: None or list[int]
      The datasets to plot.
  variables: None,list
      The variables to plot.
  adj_args
      Additional arguments passed to plt.subplots_adjust()
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code: def plot_correlations(
      self, datasets=None, variables=None, **adj_args
  ):
      """
      Plot the correlations between datasets.
      See get_correlations() for more details.
  
      Parameters
      ----------
      datasets: None or list[int]
          The datasets to plot.
      variables: None,list
          The variables to plot.
      adj_args
          Additional arguments passed to plt.subplots_adjust()
  
      """
  
      # Move this to .compat at some point.
      try:
          import matplotlib.pyplot as plt
          from matplotlib import gridspec
      except ImportError:
          raise ImportError(
              "matplotlib must be installed to plot importance"
          )
  
      if self.dataset_count() < 4:
          raise ValueError("Not enough datasets to make box plot")
      if datasets is None:
          datasets = list(range(self.dataset_count()))
      else:
          datasets = _ensure_iterable(datasets)
      var_indx = self._get_var_ind_from_list(variables)
      num_vars = self._get_num_vars(var_indx)
      plots, plotrows, plotcols = self._prep_multi_plot(num_vars)
      correlation_dict = self.get_correlations(
          datasets=datasets, variables=num_vars
      )
      gs = gridspec.GridSpec(plotrows, plotcols)
      fig, ax = plt.subplots(plotrows, plotcols, squeeze=False)
  
      for v in range(plots):
          axr, axc = next(iter(gs[v].rowspan)), next(
              iter(gs[v].colspan)
          )
          var = list(correlation_dict)[v]
          ax[axr, axc].boxplot(
              list(correlation_dict[var].values()),
              labels=range(len(correlation_dict[var])),
          )
          ax[axr, axc].set_title(self._get_var_name_from_scalar(var))
          ax[axr, axc].set_xlabel("Iteration")
          ax[axr, axc].set_ylabel("Correlations")
          ax[axr, axc].set_ylim([-1, 1])
      plt.subplots_adjust(**adj_args)
  
imports: import numpy
  from .compat import pd_DataFrame
  from .utils import _t_dat, _t_var_list, _t_var_dict, _ensure_iterable, _dict_set_diff, _assign_col_values_without_copy, _slice, _subset_data
  from itertools import combinations
  from typing import Dict, List, Union, Any, Optional
  from warnings import warn
label_desc: Use image node_id 24 for calling the ImputedData obj's underlying member method code with example usage: obj.plot_correlations(datasets, variables) without return types