code: def fclusterdata(
      X,
      t,
      criterion="inconsistent",
      metric="euclidean",
      depth=2,
      method="single",
      R=None,
  ):
      """
      Cluster observation data using a given metric.
  
      Clusters the original observations in the n-by-m data
      matrix X (n observations in m dimensions), using the euclidean
      distance metric to calculate distances between original observations,
      performs hierarchical clustering using the single linkage algorithm,
      and forms flat clusters using the inconsistency method with `t` as the
      cut-off threshold.
  
      A 1-D array ``T`` of length ``n`` is returned. ``T[i]`` is
      the index of the flat cluster to which the original observation ``i``
      belongs.
  
      Parameters
      ----------
      X : (N, M) ndarray
          N by M data matrix with N observations in M dimensions.
      t : scalar
          For criteria 'inconsistent', 'distance' or 'monocrit',
           this is the threshold to apply when forming flat clusters.
          For 'maxclust' or 'maxclust_monocrit' criteria,
           this would be max number of clusters requested.
      criterion : str, optional
          Specifies the criterion for forming flat clusters. Valid
          values are 'inconsistent' (default), 'distance', or 'maxclust'
          cluster formation algorithms. See `fcluster` for descriptions.
      metric : str or function, optional
          The distance metric for calculating pairwise distances. See
          ``distance.pdist`` for descriptions and linkage to verify
          compatibility with the linkage method.
      depth : int, optional
          The maximum depth for the inconsistency calculation. See
          `inconsistent` for more information.
      method : str, optional
          The linkage method to use (single, complete, average,
          weighted, median centroid, ward). See `linkage` for more
          information. Default is "single".
      R : ndarray, optional
          The inconsistency matrix. It will be computed if necessary
          if it is not passed.
  
      Returns
      -------
      fclusterdata : ndarray
          A vector of length n. T[i] is the flat cluster number to
          which original observation i belongs.
  
      See Also
      --------
      scipy.spatial.distance.pdist : pairwise distance metrics
  
      Notes
      -----
      This function is similar to the MATLAB function ``clusterdata``.
  
      Examples
      --------
      >>> from scipy.cluster.hierarchy import fclusterdata
  
      This is a convenience method that abstracts all the steps to perform in a
      typical SciPy's hierarchical clustering workflow.
  
      * Transform the input data into a condensed matrix with
        `scipy.spatial.distance.pdist`.
  
      * Apply a clustering method.
  
      * Obtain flat clusters at a user defined distance threshold ``t`` using
        `scipy.cluster.hierarchy.fcluster`.
  
      >>> X = [[0, 0], [0, 1], [1, 0],
      ...      [0, 4], [0, 3], [1, 4],
      ...      [4, 0], [3, 0], [4, 1],
      ...      [4, 4], [3, 4], [4, 3]]
  
      >>> fclusterdata(X, t=1)
      array([3, 3, 3, 4, 4, 4, 2, 2, 2, 1, 1, 1], dtype=int32)
  
      The output here (for the dataset ``X``, distance threshold ``t``, and the
      default settings) is four clusters with three data points each.
  
      """
      xp = array_namespace(X)
      X = as_xparray(X, order="C", dtype=xp.float64)
  
      if X.ndim != 2:
          raise TypeError(
              "The observation matrix X must be an n by m " "array."
          )
  
      Y = distance.pdist(X, metric=metric)
      Y = xp.asarray(Y)
      Z = linkage(Y, method=method)
      if R is None:
          R = inconsistent(Z, d=depth)
      else:
          R = as_xparray(R, order="c", xp=xp)
      T = fcluster(Z, criterion=criterion, depth=depth, R=R, t=t)
      return T
  
imports: import warnings
  import bisect
  from collections import deque
  import numpy
  from .None import _hierarchy, _optimal_leaf_ordering
  import scipy.spatial.distance
  from scipy._lib._array_api import array_namespace, as_xparray, copy
  from scipy._lib._disjoint_set import DisjointSet
label_desc: Use image node_id 28 for calling a global function with example usage: fclusterdata(X, t, criterion, metric, depth, method, R) and returns: T