docstr: Compare one by one embedding vs all at once using BH gradients.
code: def test_iris_bh_transform_equivalency_with_one_by_one(self):
      """Compare one by one embedding vs all at once using BH gradients."""
      x_train, x_test = train_test_split(
          self.iris.data, test_size=0.1, random_state=42
      )
  
      # Set up the initial embedding
      embedding = openTSNE.TSNE(
          early_exaggeration_iter=0,
          n_iter=50,
          neighbors="exact",
          negative_gradient_method="bh",
      ).fit(x_train)
  
      params = dict(n_iter=100, perplexity=5)
      # Build up an embedding by adding points one by one
      new_embedding_1 = np.vstack(
          [
              embedding.transform(np.atleast_2d(point), **params)
              for point in x_test
          ]
      )
      # Add new points altogether
      new_embedding_2 = embedding.transform(x_test, **params)
  
      # Verify that the embedding has actually been optimized
      self.assertRaises(
          AssertionError,
          np.testing.assert_almost_equal,
          embedding.prepare_partial(
              x_test, perplexity=params["perplexity"]
          ),
          new_embedding_1,
      )
      # Check that both methods produced the same embedding
      np.testing.assert_almost_equal(new_embedding_1, new_embedding_2)
  
imports: import unittest
  from functools import partial
  from scipy.spatial.distance import pdist, squareform
  import openTSNE
  import openTSNE.affinity
  import openTSNE.initialization
  import numpy
  from openTSNE.callbacks import VerifyExaggerationError
  from sklearn import datasets
  from sklearn.metrics import accuracy_score
  from sklearn.model_selection import train_test_split
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.manifold import SpectralEmbedding
label_desc: Use image node_id 7 for calling the TestTSNECorrectness obj's underlying member method code with example usage: obj.test_iris_bh_transform_equivalency_with_one_by_one() without return types