code: def _generate_singular_values(
      n,
      effective_rank,
      tail_strength,
      n_samples_per_part,
      dtype="float32",
  ):
      # Index of the singular values
      sing_ind = cp.arange(n, dtype=dtype)
  
      # Build the singular profile by assembling signal and noise components
      tmp = sing_ind / effective_rank
      low_rank = (1 - tail_strength) * cp.exp(-1.0 * tmp**2)
      tail = tail_strength * cp.exp(-0.1 * tmp)
      s = low_rank + tail
      return s
  
imports: from cuml.dask.common.utils import get_client
  from cuml.dask.common.input_utils import DistributedDataHandler
  from cuml.common import with_cupy_rmm
  from cuml.dask.datasets.utils import _create_delayed
  from cuml.dask.datasets.utils import _get_labels
  from cuml.dask.datasets.utils import _get_X
  from cuml.internals.safe_imports import gpu_only_import
  import dask.array
  from cuml.internals.safe_imports import cpu_only_import
label_desc: Use image node_id 10 for calling a global function with example usage: _generate_singular_values(n, effective_rank, tail_strength, n_samples_per_part, dtype) and returns: s