code: def _concrete_generators(dtype):
      d = {
          "id": lambda n, _k: _make_diag(np.ones(n)),
          "linear cond=1k": lambda n, _k: _make_diag(
              np.linspace(1, 1000, n)
          ),
          "linear cond=100k": lambda n, _k: _make_diag(
              np.linspace(1, 100 * 1000, n)
          ),
          "geom cond=1k": lambda n, _k: _make_diag(
              np.logspace(0, 3, n)
          ),
          "geom cond=100k": lambda n, _k: _make_diag(
              np.logspace(0, 5, n)
          ),
          "ring laplacian": lambda n, _k: _make_ring(n),
          "cluster(k/2)": lambda n, k: _make_cluster(k // 2, n),
          "cluster(k-1)": lambda n, k: _make_cluster(k - 1, n),
          "cluster(k)": lambda n, k: _make_cluster(k, n),
      }
  
      def cast_fn(fn):
          def casted_fn(n, k):
              result = fn(n, k)
              cast = functools.partial(np.array, dtype=dtype)
              return tuple(map(cast, result))
  
          return casted_fn
  
      return {k: cast_fn(v) for k, v in d.items()}
  
imports: import functools
  import re
  import os
  import unittest
  from absl.testing import absltest
  from absl.testing import parameterized
  import numpy
  import scipy.linalg
  import scipy.sparse
  import jax
  from jax import config
  from jax._src import test_util
  from jax.experimental.sparse import linalg, bcoo
  import jax.numpy
label_desc: Use image node_id 7 for calling a global function with example usage: _concrete_generators(dtype) and returns: unknown, casted_fn, tuple