code: def pointwise_ops(self):
      a = torch.randn(4)
      b = torch.randn(4)
      t = torch.tensor([-1, -2, 3], dtype=torch.int8)
      r = torch.tensor([0, 1, 10, 0], dtype=torch.int8)
      t = torch.tensor([-1, -2, 3], dtype=torch.int8)
      s = torch.tensor([4, 0, 1, 0], dtype=torch.int8)
      f = torch.zeros(3)
      g = torch.tensor([-1, 0, 1])
      w = torch.tensor([0.3810, 1.2774, -0.2972, -0.3719, 0.4637])
      return len(
          torch.abs(torch.tensor([-1, -2, 3])),
          torch.absolute(torch.tensor([-1, -2, 3])),
          torch.acos(a),
          torch.arccos(a),
          torch.acosh(a.uniform_(1.0, 2.0)),
          torch.add(a, 20),
          torch.add(a, b, out=a),
          b.add(a),
          b.add(a, out=b),
          b.add_(a),
          b.add(1),
          torch.add(a, torch.randn(4, 1), alpha=10),
          torch.addcdiv(
              torch.randn(1, 3),
              torch.randn(3, 1),
              torch.randn(1, 3),
              value=0.1,
          ),
          torch.addcmul(
              torch.randn(1, 3),
              torch.randn(3, 1),
              torch.randn(1, 3),
              value=0.1,
          ),
          torch.angle(a),
          torch.asin(a),
          torch.arcsin(a),
          torch.asinh(a),
          torch.arcsinh(a),
          torch.atan(a),
          torch.arctan(a),
          torch.atanh(a.uniform_(-1.0, 1.0)),
          torch.arctanh(a.uniform_(-1.0, 1.0)),
          torch.atan2(a, a),
          torch.bitwise_not(t),
          torch.bitwise_and(
              t, torch.tensor([1, 0, 3], dtype=torch.int8)
          ),
          torch.bitwise_or(
              t, torch.tensor([1, 0, 3], dtype=torch.int8)
          ),
          torch.bitwise_xor(
              t, torch.tensor([1, 0, 3], dtype=torch.int8)
          ),
          torch.ceil(a),
          torch.ceil(float(torch.tensor(0.5))),
          torch.ceil(torch.tensor(0.5).item()),
          torch.clamp(a, min=-0.5, max=0.5),
          torch.clamp(a, min=0.5),
          torch.clamp(a, max=0.5),
          torch.clip(a, min=-0.5, max=0.5),
          torch.conj(a),
          torch.copysign(a, 1),
          torch.copysign(a, b),
          torch.cos(a),
          torch.cosh(a),
          torch.deg2rad(
              torch.tensor(
                  [[180.0, -180.0], [360.0, -360.0], [90.0, -90.0]]
              )
          ),
          torch.div(a, b),
          a.div(b),
          a.div(1),
          a.div_(b),
          torch.divide(a, b, rounding_mode="trunc"),
          torch.divide(a, b, rounding_mode="floor"),
          torch.digamma(torch.tensor([1.0, 0.5])),
          torch.erf(torch.tensor([0.0, -1.0, 10.0])),
          torch.erfc(torch.tensor([0.0, -1.0, 10.0])),
          torch.erfinv(torch.tensor([0.0, 0.5, -1.0])),
          torch.exp(torch.tensor([0.0, math.log(2.0)])),
          torch.exp(float(torch.tensor(1))),
          torch.exp2(torch.tensor([0.0, math.log(2.0), 3.0, 4.0])),
          torch.expm1(torch.tensor([0.0, math.log(2.0)])),
          torch.fake_quantize_per_channel_affine(
              torch.randn(2, 2, 2),
              (torch.randn(2) + 1) * 0.05,
              torch.zeros(2),
              1,
              0,
              255,
          ),
          torch.fake_quantize_per_tensor_affine(a, 0.1, 0, 0, 255),
          torch.float_power(torch.randint(10, (4,)), 2),
          torch.float_power(
              torch.arange(1, 5), torch.tensor([2, -3, 4, -5])
          ),
          torch.floor(a),
          torch.floor(float(torch.tensor(1))),
          torch.floor_divide(
              torch.tensor([4.0, 3.0]), torch.tensor([2.0, 2.0])
          ),
          torch.floor_divide(torch.tensor([4.0, 3.0]), 1.4),
          torch.fmod(torch.tensor([-3, -2, -1, 1, 2, 3]), 2),
          torch.fmod(torch.tensor([1, 2, 3, 4, 5]), 1.5),
          torch.frac(torch.tensor([1.0, 2.5, -3.2])),
          torch.randn(4, dtype=torch.cfloat).imag,
          torch.ldexp(torch.tensor([1.0]), torch.tensor([1])),
          torch.ldexp(torch.tensor([1.0]), torch.tensor([1, 2, 3, 4])),
          torch.lerp(
              torch.arange(1.0, 5.0), torch.empty(4).fill_(10), 0.5
          ),
          torch.lerp(
              torch.arange(1.0, 5.0),
              torch.empty(4).fill_(10),
              torch.full_like(torch.arange(1.0, 5.0), 0.5),
          ),
          torch.lgamma(torch.arange(0.5, 2, 0.5)),
          torch.log(torch.arange(5) + 10),
          torch.log10(torch.rand(5)),
          torch.log1p(torch.randn(5)),
          torch.log2(torch.rand(5)),
          torch.logaddexp(
              torch.tensor([-1.0]), torch.tensor([-1, -2, -3])
          ),
          torch.logaddexp(
              torch.tensor([-100.0, -200.0, -300.0]),
              torch.tensor([-1, -2, -3]),
          ),
          torch.logaddexp(
              torch.tensor([1.0, 2000.0, 30000.0]),
              torch.tensor([-1, -2, -3]),
          ),
          torch.logaddexp2(
              torch.tensor([-1.0]), torch.tensor([-1, -2, -3])
          ),
          torch.logaddexp2(
              torch.tensor([-100.0, -200.0, -300.0]),
              torch.tensor([-1, -2, -3]),
          ),
          torch.logaddexp2(
              torch.tensor([1.0, 2000.0, 30000.0]),
              torch.tensor([-1, -2, -3]),
          ),
          torch.logical_and(r, s),
          torch.logical_and(r.double(), s.double()),
          torch.logical_and(r.double(), s),
          torch.logical_and(r, s, out=torch.empty(4, dtype=torch.bool)),
          torch.logical_not(
              torch.tensor([0, 1, -10], dtype=torch.int8)
          ),
          torch.logical_not(
              torch.tensor([0.0, 1.5, -10.0], dtype=torch.double)
          ),
          torch.logical_not(
              torch.tensor([0.0, 1.0, -10.0], dtype=torch.double),
              out=torch.empty(3, dtype=torch.int16),
          ),
          torch.logical_or(r, s),
          torch.logical_or(r.double(), s.double()),
          torch.logical_or(r.double(), s),
          torch.logical_or(r, s, out=torch.empty(4, dtype=torch.bool)),
          torch.logical_xor(r, s),
          torch.logical_xor(r.double(), s.double()),
          torch.logical_xor(r.double(), s),
          torch.logical_xor(r, s, out=torch.empty(4, dtype=torch.bool)),
          torch.logit(torch.rand(5), eps=1e-6),
          torch.hypot(
              torch.tensor([4.0]), torch.tensor([3.0, 4.0, 5.0])
          ),
          torch.i0(torch.arange(5, dtype=torch.float32)),
          torch.igamma(a, b),
          torch.igammac(a, b),
          torch.mul(torch.randn(3), 100),
          b.mul(a),
          b.mul(5),
          b.mul(a, out=b),
          b.mul_(a),
          b.mul_(5),
          torch.multiply(torch.randn(4, 1), torch.randn(1, 4)),
          torch.mvlgamma(torch.empty(2, 3).uniform_(1.0, 2.0), 2),
          torch.tensor(
              [float("nan"), float("inf"), -float("inf"), 3.14]
          ),
          torch.nan_to_num(w),
          torch.nan_to_num_(w),
          torch.nan_to_num(w, nan=2.0),
          torch.nan_to_num(w, nan=2.0, posinf=1.0),
          torch.neg(torch.randn(5)),
          # torch.nextafter(torch.tensor([1, 2]), torch.tensor([2, 1])) == torch.tensor([eps + 1, 2 - eps]),
          torch.polygamma(1, torch.tensor([1.0, 0.5])),
          torch.polygamma(2, torch.tensor([1.0, 0.5])),
          torch.polygamma(3, torch.tensor([1.0, 0.5])),
          torch.polygamma(4, torch.tensor([1.0, 0.5])),
          torch.pow(a, 2),
          torch.pow(2, float(torch.tensor(0.5))),
          torch.pow(torch.arange(1.0, 5.0), torch.arange(1.0, 5.0)),
          torch.rad2deg(
              torch.tensor(
                  [[3.142, -3.142], [6.283, -6.283], [1.570, -1.570]]
              )
          ),
          torch.randn(4, dtype=torch.cfloat).real,
          torch.reciprocal(a),
          torch.remainder(torch.tensor([-3.0, -2.0]), 2),
          torch.remainder(torch.tensor([1, 2, 3, 4, 5]), 1.5),
          torch.round(a),
          torch.round(torch.tensor(0.5).item()),
          torch.rsqrt(a),
          torch.sigmoid(a),
          torch.sign(torch.tensor([0.7, -1.2, 0.0, 2.3])),
          torch.sgn(a),
          torch.signbit(torch.tensor([0.7, -1.2, 0.0, 2.3])),
          torch.sin(a),
          torch.sinc(a),
          torch.sinh(a),
          torch.sqrt(a),
          torch.square(a),
          torch.sub(
              torch.tensor((1, 2)), torch.tensor((0, 1)), alpha=2
          ),
          b.sub(a),
          b.sub_(a),
          b.sub(5),
          torch.sum(5),
          torch.tan(a),
          torch.tanh(a),
          torch.true_divide(a, a),
          torch.trunc(a),
          torch.trunc_(a),
          torch.xlogy(f, g),
          torch.xlogy(f, g),
          torch.xlogy(f, 4),
          torch.xlogy(2, g),
      )
  
imports: import math
  import torch
label_desc: Use image node_id 2 for calling the PointwiseOpsModule obj's underlying member method code with example usage: obj.pointwise_ops() and returns: len