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: Unify embedding length of neighborhood information for efficiency consideration.
  Truncate the list if the length is larger than max_ner.
  Otherwise, pad it with 0.
  
  Args:
      lst (list): The input list contains node's neighbors.
      max_ner (int): The number of neighbors we choose for each node.
  
  Returns:
      list: The list of a node's community neighbors.
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code: def Max_ner(self, lst, max_ner):
      r"""Unify embedding length of neighborhood information for efficiency consideration.
      Truncate the list if the length is larger than max_ner.
      Otherwise, pad it with 0.
  
      Args:
          lst (list): The input list contains node's neighbors.
          max_ner (int): The number of neighbors we choose for each node.
  
      Returns:
          list: The list of a node's community neighbors.
  
  
      """
      for i in range(len(lst)):
          if len(lst[i]) >= max_ner:
              lst[i] = lst[i][:max_ner]
          else:
              length = len(lst[i])
              for _ in range(max_ner - length):
                  lst[i].append(0)
      return lst
  
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 3 for calling the NNCF obj's underlying member method code with example usage: obj.Max_ner(lst, max_ner) and returns: lst