code: def value(cls, dtype):
      if torch.is_tensor(dtype):
          dtype = dtype.dtype
      if dtype == torch.float:
          return cls._global_float_value
      elif dtype == torch.double:
          return cls._global_double_value
      elif dtype == torch.half:
          return cls._global_half_value
      else:
          raise RuntimeError(f"Unsupported dtype for {cls.__name__}.")
  
imports: import torch
  from linear_operator.settings import _linalg_dtype_cholesky, _linalg_dtype_symeig, cg_tolerance, cholesky_jitter, cholesky_max_tries, ciq_samples, deterministic_probes, fast_computations, linalg_dtypes, max_cg_iterations, max_cholesky_size, max_lanczos_quadrature_iterations, max_preconditioner_size, max_root_decomposition_size, min_preconditioning_size, minres_tolerance, num_contour_quadrature, num_trace_samples, preconditioner_tolerance, skip_logdet_forward, terminate_cg_by_size, tridiagonal_jitter, use_toeplitz, verbose_linalg
  from torch import Tensor
label_desc: Use image node_id 1 for calling the _dtype_value_context obj's underlying member method code with example usage: obj.value(cls, dtype) and returns: cls, cls, cls