class_docstr: Neo4jStellarGraph class for graph machine learning on graphs stored in
  a Neo4j database.
  
  This class communicates with Neo4j via a :class:`py2neo.Graph` connected to the graph
  database of interest and contains functions to query the graph data necessary
  for machine learning.
  
  .. seealso:: `Examples using Neoj <https://stellargraph.readthedocs.io/en/stable/demos/connector/neo4j/index.html>`__.
  
  Args:
      graph_db (py2neo.Graph): a :class:`py2neo.Graph` connected to a Neo4j graph database.
      node_label (str, optional): Common label for all nodes in the graph, if such label exists.
          Providing this is useful if there are any indexes created on this label (e.g. on node IDs),
          as it will improve performance of queries.
      id_property (str, optional): Name of Neo4j property to use as ID.
      features_property (str, optional): Name of Neo4j property to use as features.
      is_directed (bool, optional): If True, the data represents a
          directed multigraph, otherwise an undirected multigraph.
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docstr: Load all node IDs and features into memory from Neo4j so that subsequent method calls that
  access node features can use the cached data instead of querying the database.
  
  This method should be avoided for larger graphs.
  
  Args:
      dtype (str, optional): Data type of features
docstr_tok: ["Load","all","node","IDs","and","features","into","memory","from","Neo4j","so","that","subsequent","method","calls","that","access","node","features","can","use","the","cached","data","instead","of","querying","the","database",".","This","method","should","be","avoided","for","larger","graphs",".","Args",":","dtype","(","str",",","optional",")",":","Data","type","of","features"]
code: def cache_all_nodes_in_memory(self, dtype="float32"):
      """
      Load all node IDs and features into memory from Neo4j so that subsequent method calls that
      access node features can use the cached data instead of querying the database.
  
      This method should be avoided for larger graphs.
  
      Args:
          dtype (str, optional): Data type of features
  
      """
      features = self._node_features_from_db(None)
      self._nodes = convert.convert_nodes(
          features,
          name="nodes",
          default_type=self._node_type,
          dtype=dtype,
      )
  
      # cache feature size too
      self._node_feature_size = self._nodes.features_of_type(
          self._node_type
      ).shape[1]
  
imports: import numpy
  import scipy.sparse
  import pandas
  import re
  import warnings
  from ...None import globalvar
  from ...core.experimental import experimental
  from ...core import convert
  from ...core.indexed_array import IndexedArray
  from ...core.graph import extract_element_features
  from ...core.validation import comma_sep
label_desc: Use image node_id 4 for calling the Neo4jStellarGraph obj's underlying member method code with example usage: obj.cache_all_nodes_in_memory(dtype) without return types