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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docstr: Gradient of log sigmoid function.
  
  Parameters
  ----------
  vector : array-like, shape=[n_samples, dim]
  
  Returns
  -------
  gradient : array-like, shape=[n_samples, dim]
docstr_tok: ["Gradient","of","log","sigmoid","function",".","Parameters","--","--","--","--","--","vector",":","array-like",",","shape=","[","n_samples",",","dim","]","Returns","--","--","--","-","gradient",":","array-like",",","shape=","[","n_samples",",","dim","]"]
code: def grad_log_sigmoid(vector):
      """Gradient of log sigmoid function.
  
      Parameters
      ----------
      vector : array-like, shape=[n_samples, dim]
  
      Returns
      -------
      gradient : array-like, shape=[n_samples, dim]
      """
      return 1 / (1 + gs.exp(vector))
  
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 3 for calling the HyperbolicEmbedding obj's underlying member method code with example usage: obj.grad_log_sigmoid(vector) and returns: unknown