code: def display_embeddings(
      train_dataset: TextData,
      test_dataset: TextData,
      random_state: int,
      model_classes: list,
  ):
      """Display the embeddings with the domain classifier proba as the x-axis and the embeddings as the y-axis."""
      embeddings = np.concatenate(
          [train_dataset.embeddings, test_dataset.embeddings]
      )
  
      reducer = UMAP(
          n_components=2,
          n_neighbors=5,
          init="random",
          min_dist=1,
          random_state=random_state,
      )
      reduced_embeddings = reducer.fit_transform(embeddings)
  
      x_axis_title = "Reduced Embedding (0)"
      y_axis_title = "Reduced Embedding (1)"
  
      plot_data = pd.DataFrame(
          {
              x_axis_title: reduced_embeddings[:, 0],
              y_axis_title: reduced_embeddings[:, 1],
          }
      )
      plot_title = (
          "Scatter Plot of Embeddings Space (reduced to 2 dimensions)"
      )
      return two_datasets_scatter_plot(
          plot_title=plot_title,
          plot_data=plot_data,
          train_dataset=train_dataset,
          test_dataset=test_dataset,
          model_classes=model_classes,
      )
  
file_path: deepchecks/deepchecks/nlp/utils/multivariate_embeddings_drift_utils.py
imports: import warnings
  import numpy
  import pandas
  from numba import NumbaDeprecationWarning
  from sklearn.decomposition import PCA
  from sklearn.ensemble import GradientBoostingClassifier
  from sklearn.metrics import roc_auc_score
  from sklearn.model_selection import train_test_split
  from deepchecks.core.check_utils.multivariate_drift_utils import auc_to_drift_score, build_drift_plot
  from deepchecks.nlp import TextData
  from deepchecks.nlp.utils.nlp_plot import two_datasets_scatter_plot
label_desc: Use image node_id 2 for calling a global function with example usage: display_embeddings(train_dataset, test_dataset, random_state, model_classes) and returns: two_datasets_scatter_plot