class_docstr: SGL is a GCN-based recommender model.
  
  SGL supplements the classical supervised task of recommendation with an auxiliary
  self supervised task, which reinforces node representation learning via self-
  discrimination.Specifically,SGL generates multiple views of a node, maximizing the
  agreement between different views of the same node compared to that of other nodes.
  SGL devises three operators to generate the views — node dropout, edge dropout, and
  random walk — that change the graph structure in different manners.
  
  We implement the model following the original author with a pairwise training mode.
docstr: Randomly discard some points or edges.
  
  Args:
      high (int): Upper limit of index value
      size (int): Array size after sampling
  
  Returns:
      numpy.ndarray: Array index after sampling, shape: [size]
docstr_tok: ["Randomly","discard","some","points","or","edges",".","Args",":","high","(","int",")",":","Upper","limit","of","index","value","size","(","int",")",":","Array","size","after","sampling","Returns",":","numpy.ndarray",":","Array","index","after","sampling",",","shape",":","[","size","]"]
code: def rand_sample(self, high, size=None, replace=True):
      r"""Randomly discard some points or edges.
  
      Args:
          high (int): Upper limit of index value
          size (int): Array size after sampling
  
      Returns:
          numpy.ndarray: Array index after sampling, shape: [size]
      """
  
      a = np.arange(high)
      sample = np.random.choice(a, size=size, replace=replace)
      return sample
  
imports: import numpy
  import scipy.sparse
  import torch
  from recbole.model.abstract_recommender import GeneralRecommender
  from recbole.model.init import xavier_uniform_initialization
  from recbole.model.loss import BPRLoss, EmbLoss
  from recbole.utils import InputType
  import torch.nn.functional
label_desc: Use image node_id 3 for calling the SGL obj's underlying member method code with example usage: obj.rand_sample(high, size, replace) and returns: sample