code: def test_linear_chain_counting(batch, N, C):
      vals = torch.ones(batch, N, C, C)
      semiring = StdSemiring
      alpha = LinearChain(semiring).sum(vals)
      c = pow(C, N + 1)
      assert (alpha == c).all()
  
imports: from torch_struct import CKY, CKY_CRF, DepTree, LinearChain, SemiMarkov, Alignment, deptree_nonproj, deptree_part
  from torch_struct import LogSemiring, CheckpointSemiring, CheckpointShardSemiring, KMaxSemiring, SparseMaxSemiring, MaxSemiring, StdSemiring, EntropySemiring
  from .extensions import LinearChainTest, SemiMarkovTest, DepTreeTest, CKYTest, CKY_CRFTest, test_lookup
  import torch
  from hypothesis import given
  from hypothesis.strategies import integers, data, sampled_from
  import pytest
  from hypothesis import settings
label_desc: Use image node_id 1 for calling a global function with example usage: test_linear_chain_counting(batch, N, C) without return types