code: def __init__(
      self,
      n_neighbors=15,
      n_components=2,
      metric="euclidean",
      metric_kwds=None,
      n_epochs=None,
      learning_rate=1.0,
      init="spectral",
      alignment_regularisation=1.0e-2,
      alignment_window_size=3,
      min_dist=0.1,
      spread=1.0,
      low_memory=False,
      set_op_mix_ratio=1.0,
      local_connectivity=1.0,
      repulsion_strength=1.0,
      negative_sample_rate=5,
      transform_queue_size=4.0,
      a=None,
      b=None,
      random_state=None,
      angular_rp_forest=False,
      target_n_neighbors=-1,
      target_metric="categorical",
      target_metric_kwds=None,
      target_weight=0.5,
      transform_seed=42,
      force_approximation_algorithm=False,
      verbose=False,
      unique=False,
  ):
      self.n_neighbors = n_neighbors
      self.metric = metric
      self.metric_kwds = metric_kwds
  
      self.n_epochs = n_epochs
      self.init = init
      self.n_components = n_components
      self.repulsion_strength = repulsion_strength
      self.learning_rate = learning_rate
      self.alignment_regularisation = alignment_regularisation
      self.alignment_window_size = alignment_window_size
  
      self.spread = spread
      self.min_dist = min_dist
      self.low_memory = low_memory
      self.set_op_mix_ratio = set_op_mix_ratio
      self.local_connectivity = local_connectivity
      self.negative_sample_rate = negative_sample_rate
      self.random_state = random_state
      self.angular_rp_forest = angular_rp_forest
      self.transform_queue_size = transform_queue_size
      self.target_n_neighbors = target_n_neighbors
      self.target_metric = target_metric
      self.target_metric_kwds = target_metric_kwds
      self.target_weight = target_weight
      self.transform_seed = transform_seed
      self.force_approximation_algorithm = force_approximation_algorithm
      self.verbose = verbose
      self.unique = unique
  
      self.a = a
      self.b = b
  
imports: import numpy
  import numba
  from sklearn.base import BaseEstimator
  from sklearn.utils import check_array
  from umap.sparse import arr_intersect
  from umap.sparse import arr_union
  from umap.umap_ import UMAP, make_epochs_per_sample
  from umap.spectral import spectral_layout
  from umap.layouts import optimize_layout_aligned_euclidean
label_desc: Use image node_id 1 to create a new AlignedUMAP object from inherited base classes: BaseEstimator with example: obj = AlignedUMAP(n_neighbors, n_components, metric, metric_kwds, n_epochs, learning_rate, init, alignment_regularisation, alignment_window_size, min_dist, spread, low_memory, set_op_mix_ratio, local_connectivity, repulsion_strength, negative_sample_rate, transform_queue_size, a, b, random_state, angular_rp_forest, target_n_neighbors, target_metric, target_metric_kwds, target_weight, transform_seed, force_approximation_algorithm, verbose, unique)