class_docstr: The jitter value used for Cholesky factorizations in variational models.
  
  - Default for `float`: 1e-4
  - Default for `double`: 1e-6
class_docstr_tok: ["The","jitter","value","used","for","Cholesky","factorizations","in","variational","models",".","-","Default","for","`","float","`",":","1e-4","-","Default","for","`","double","`",":","1e-6"]
code: def value(cls, dtype=None):
      return super().value(dtype=dtype)
  
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 variational_cholesky_jitter obj's underlying member method code with example usage: obj.value(cls, dtype) and returns: super