code: def __init__(
      self, float_value=None, double_value=None, half_value=None
  ):
      self._orig_float_value = self.__class__.value(torch.float)
      self._instance_float_value = (
          float_value
          if float_value is not None
          else self._orig_float_value
      )
      self._orig_double_value = self.__class__.value(torch.double)
      self._instance_double_value = (
          double_value
          if double_value is not None
          else self._orig_double_value
      )
      self._orig_half_value = self.__class__.value(torch.half)
      self._instance_half_value = (
          half_value
          if half_value is not None
          else self._orig_half_value
      )
  
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 3 to create a new _dtype_value_context object  with example: obj = _dtype_value_context(float_value, double_value, half_value)