code: def logpotentials(draw, min_n=2):
      b = draw(integers(min_value=2, max_value=3))
      N = draw(integers(min_value=min_n, max_value=3))
      C = draw(integers(min_value=2, max_value=3))
      logp = draw(
          arrays(
              np.float,
              (b, N, C, C),
              floats(min_value=-100.0, max_value=100.0),
          )
      )
      return torch.tensor(logp), (b, (N + 1))
  
imports: import torch_struct
  import torch
  from torch_struct import LogSemiring
  import itertools
  from hypothesis.strategies import integers, composite, floats
  from hypothesis.extra.numpy import arrays
  import numpy
label_desc: Use image node_id 1 for calling the LinearChainTest obj's underlying member method code with example usage: obj.logpotentials(draw, min_n) and returns: torch,  b,  unknown