class_docstr: Agglomerative Clustering.
  
  Recursively merges pair of clusters of sample data; uses linkage distance.
  
  Read more in the :ref:`User Guide <hierarchical_clustering>`.
  
  Parameters
  ----------
  n_clusters : int or None, default=2
      The number of clusters to find. It must be ``None`` if
      ``distance_threshold`` is not ``None``.
  
  affinity : str or callable, default='euclidean'
      The metric to use when calculating distance between instances in a
      feature array. If metric is a string or callable, it must be one of
      the options allowed by :func:`sklearn.metrics.pairwise_distances` for
      its metric parameter.
      If linkage is "ward", only "euclidean" is accepted.
      If "precomputed", a distance matrix (instead of a similarity matrix)
      is needed as input for the fit method.
  
      .. deprecated:: 1.2
          `affinity` was deprecated in version 1.2 and will be renamed to
          `metric` in 1.4.
  
  metric : str or callable, default=None
      Metric used to compute the linkage. Can be "euclidean", "l1", "l2",
      "manhattan", "cosine", or "precomputed". If set to `None` then
      "euclidean" is used. If linkage is "ward", only "euclidean" is
      accepted. If "precomputed", a distance matrix is needed as input for
      the fit method.
  
      .. versionadded:: 1.2
  
  memory : str or object with the joblib.Memory interface, default=None
      Used to cache the output of the computation of the tree.
      By default, no caching is done. If a string is given, it is the
      path to the caching directory.
  
  connectivity : array-like or callable, default=None
      Connectivity matrix. Defines for each sample the neighboring
      samples following a given structure of the data.
      This can be a connectivity matrix itself or a callable that transforms
      the data into a connectivity matrix, such as derived from
      `kneighbors_graph`. Default is ``None``, i.e, the
      hierarchical clustering algorithm is unstructured.
  
  compute_full_tree : 'auto' or bool, default='auto'
      Stop early the construction of the tree at ``n_clusters``. This is
      useful to decrease computation time if the number of clusters is not
      small compared to the number of samples. This option is useful only
      when specifying a connectivity matrix. Note also that when varying the
      number of clusters and using caching, it may be advantageous to compute
      the full tree. It must be ``True`` if ``distance_threshold`` is not
      ``None``. By default `compute_full_tree` is "auto", which is equivalent
      to `True` when `distance_threshold` is not `None` or that `n_clusters`
      is inferior to the maximum between 100 or `0.02 * n_samples`.
      Otherwise, "auto" is equivalent to `False`.
  
  linkage : {'ward', 'complete', 'average', 'single'}, default='ward'
      Which linkage criterion to use. The linkage criterion determines which
      distance to use between sets of observation. The algorithm will merge
      the pairs of cluster that minimize this criterion.
  
      - 'ward' minimizes the variance of the clusters being merged.
      - 'average' uses the average of the distances of each observation of
        the two sets.
      - 'complete' or 'maximum' linkage uses the maximum distances between
        all observations of the two sets.
      - 'single' uses the minimum of the distances between all observations
        of the two sets.
  
      .. versionadded:: 0.20
          Added the 'single' option
  
  distance_threshold : float, default=None
      The linkage distance threshold at or above which clusters will not be
      merged. If not ``None``, ``n_clusters`` must be ``None`` and
      ``compute_full_tree`` must be ``True``.
  
      .. versionadded:: 0.21
  
  compute_distances : bool, default=False
      Computes distances between clusters even if `distance_threshold` is not
      used. This can be used to make dendrogram visualization, but introduces
      a computational and memory overhead.
  
      .. versionadded:: 0.24
  
  Attributes
  ----------
  n_clusters_ : int
      The number of clusters found by the algorithm. If
      ``distance_threshold=None``, it will be equal to the given
      ``n_clusters``.
  
  labels_ : ndarray of shape (n_samples)
      Cluster labels for each point.
  
  n_leaves_ : int
      Number of leaves in the hierarchical tree.
  
  n_connected_components_ : int
      The estimated number of connected components in the graph.
  
      .. versionadded:: 0.21
          ``n_connected_components_`` was added to replace ``n_components_``.
  
  n_features_in_ : int
      Number of features seen during :term:`fit`.
  
      .. versionadded:: 0.24
  
  feature_names_in_ : ndarray of shape (`n_features_in_`,)
      Names of features seen during :term:`fit`. Defined only when `X`
      has feature names that are all strings.
  
      .. versionadded:: 1.0
  
  children_ : array-like of shape (n_samples-1, 2)
      The children of each non-leaf node. Values less than `n_samples`
      correspond to leaves of the tree which are the original samples.
      A node `i` greater than or equal to `n_samples` is a non-leaf
      node and has children `children_[i - n_samples]`. Alternatively
      at the i-th iteration, children[i][0] and children[i][1]
      are merged to form node `n_samples + i`.
  
  distances_ : array-like of shape (n_nodes-1,)
      Distances between nodes in the corresponding place in `children_`.
      Only computed if `distance_threshold` is used or `compute_distances`
      is set to `True`.
  
  See Also
  --------
  FeatureAgglomeration : Agglomerative clustering but for features instead of
      samples.
  ward_tree : Hierarchical clustering with ward linkage.
  
  Examples
  --------
  >>> from sklearn.cluster import AgglomerativeClustering
  >>> import numpy as np
  >>> X = np.array([[1, 2], [1, 4], [1, 0],
  ...               [4, 2], [4, 4], [4, 0]])
  >>> clustering = AgglomerativeClustering().fit(X)
  >>> clustering
  AgglomerativeClustering()
  >>> clustering.labels_
  array([1, 1, 1, 0, 0, 0])
docstr: Fit and return the result of each sample's clustering assignment.
  
  In addition to fitting, this method also return the result of the
  clustering assignment for each sample in the training set.
  
  Parameters
  ----------
  X : array-like of shape (n_samples, n_features) or                 (n_samples, n_samples)
      Training instances to cluster, or distances between instances if
      ``affinity='precomputed'``.
  
  y : Ignored
      Not used, present here for API consistency by convention.
  
  Returns
  -------
  labels : ndarray of shape (n_samples,)
      Cluster labels.
code: def fit_predict(self, X, y=None):
      """Fit and return the result of each sample's clustering assignment.
  
          In addition to fitting, this method also return the result of the
          clustering assignment for each sample in the training set.
  
          Parameters
          ----------
          X : array-like of shape (n_samples, n_features) or \
                  (n_samples, n_samples)
              Training instances to cluster, or distances between instances if
              ``affinity='precomputed'``.
  
          y : Ignored
              Not used, present here for API consistency by convention.
  
          Returns
          -------
          labels : ndarray of shape (n_samples,)
              Cluster labels.
          """
      return super().fit_predict(X, y)
  
imports: import warnings
  from heapq import heapify, heappop, heappush, heappushpop
  from numbers import Integral, Real
  import numpy
  from scipy import sparse
  from scipy.sparse.csgraph import connected_components
  from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, ClusterMixin, _fit_context
  from ..metrics import DistanceMetric
  from ..metrics._dist_metrics import METRIC_MAPPING64
  from ..metrics.pairwise import _VALID_METRICS, paired_distances
  from ..utils import check_array
  from ..utils._fast_dict import IntFloatDict
  from ..utils._param_validation import HasMethods, Hidden, Interval, StrOptions, validate_params
  from ..utils.graph import _fix_connected_components
  from ..utils.validation import check_memory
  from .None import _hierarchical_fast
  from ._feature_agglomeration import AgglomerationTransform
label_desc: Use image node_id 4 for calling the AgglomerativeClustering obj's underlying member method code with example usage: obj.fit_predict(X, y) and returns: super