class_docstr: NNCF is an neural network enhanced matrix factorization model which also captures neighborhood information.
  We implement the NNCF model with three ways to process neighborhood information.
docstr: Find other nodes in the same community.
  e.g. If the node starts with letter "i",
  the other nodes start with letter "i" in the same community dict group are its community neighbors.
  
  Args:
      partition (dict): The input dict that contains the community each node belongs.
      community_dict (dict): The input dict that shows the nodes each community contains.
      node (int): The id of the input node.
      kind (char): The type of the input node.
  
  Returns:
      list: The list of a node's community neighbors.
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code: def get_community_member(self, partition, community_dict, node, kind):
      r"""Find other nodes in the same community.
      e.g. If the node starts with letter "i",
      the other nodes start with letter "i" in the same community dict group are its community neighbors.
  
      Args:
          partition (dict): The input dict that contains the community each node belongs.
          community_dict (dict): The input dict that shows the nodes each community contains.
          node (int): The id of the input node.
          kind (char): The type of the input node.
  
      Returns:
          list: The list of a node's community neighbors.
  
      """
      comm = community_dict[partition[node]]
      return [x for x in comm if x.startswith(kind)]
  
imports: import torch
  import torch.nn
  from torch.nn.init import normal_
  from recbole.model.abstract_recommender import GeneralRecommender
  from recbole.model.layers import MLPLayers
  from recbole.utils import InputType
  from recbole.model.general_recommender.itemknn import ComputeSimilarity
  import numpy
label_desc: Use image node_id 4 for calling the NNCF obj's underlying member method code with example usage: obj.get_community_member(partition, community_dict, node, kind) and returns: unknown