code: def reduction_ops(self):
      a = torch.randn(4)
      b = torch.randn(4)
      c = torch.tensor(0.5)
      return len(
          torch.argmax(a),
          torch.argmin(a),
          torch.amax(a),
          torch.amin(a),
          torch.aminmax(a),
          torch.all(a),
          torch.any(a),
          torch.max(a),
          a.max(a),
          torch.max(a, 0),
          torch.min(a),
          a.min(a),
          torch.min(a, 0),
          torch.dist(a, b),
          torch.logsumexp(a, 0),
          torch.mean(a),
          torch.mean(a, 0),
          torch.nanmean(a),
          torch.median(a),
          torch.nanmedian(a),
          torch.mode(a),
          torch.norm(a),
          a.norm(2),
          torch.norm(a, dim=0),
          torch.norm(c, torch.tensor(2)),
          torch.nansum(a),
          torch.prod(a),
          torch.quantile(a, torch.tensor([0.25, 0.5, 0.75])),
          torch.quantile(a, 0.5),
          torch.nanquantile(a, torch.tensor([0.25, 0.5, 0.75])),
          torch.std(a),
          torch.std_mean(a),
          torch.sum(a),
          torch.unique(a),
          torch.unique_consecutive(a),
          torch.var(a),
          torch.var_mean(a),
          torch.count_nonzero(a),
      )
  
imports: import math
  import torch
label_desc: Use image node_id 2 for calling the ReductionOpsModule obj's underlying member method code with example usage: obj.reduction_ops() and returns: len