class_docstr: Class for learning embeddings of graphs on hyperbolic space.
  
  Parameters
  ----------
  dim : object
      Dimensions of the used hyperbolic space.
  max_epochs : int
      Maximum number of iterations for embedding.
  lr : int
      Learning rate for embedding.
  n_context : int
      Number of nodes to consider from a neighborhood
      of nodes around a particular node.
  n_negative : int
      Number of nodes to consider when searching for
      a set of nodes that are far from a particular node.
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code: def __init__(
      self, dim=2, max_epochs=100, lr=0.05, n_context=1, n_negative=2
  ):
      self.manifold = PoincareBall(dim)
      self.max_epochs = max_epochs
      self.lr = lr
      self.n_context = n_context
      self.n_negative = n_negative
  
file_path: geomstats/geomstats/datasets/prepare_graph_data.py
imports: import logging
  import random
  import geomstats.backend
  from geomstats.geometry.poincare_ball import PoincareBall
label_desc: Use image node_id 1 to create a new HyperbolicEmbedding object  with example: obj = HyperbolicEmbedding(dim, max_epochs, lr, n_context, n_negative)