code: def forward(self):
      a = torch.empty(3, 3).uniform_(0.0, 1.0)
      size = (1, 4)
      weights = torch.tensor([0, 10, 3, 0], dtype=torch.float)
      return len(
          # torch.seed(),
          # torch.manual_seed(0),
          torch.bernoulli(a),
          # torch.initial_seed(),
          torch.multinomial(weights, 2),
          torch.normal(2.0, 3.0, size),
          torch.poisson(a),
          torch.rand(2, 3),
          torch.rand_like(a),
          torch.randint(10, size),
          torch.randint_like(a, 4),
          torch.rand(4),
          torch.randn_like(a),
          torch.randperm(4),
          a.bernoulli_(),
          a.cauchy_(),
          a.exponential_(),
          a.geometric_(0.5),
          a.log_normal_(),
          a.normal_(),
          a.random_(),
          a.uniform_(),
      )
  
label_desc: Use image node_id 1 for calling the SamplingOpsModule obj's underlying member method code with example usage: obj.forward() and returns: len