code: def matrix_is_stable(x, cond_limit=30):
      """Check if a matrix is numerically stable or not.
  
      Used to avoid numerical instabilities in further computationally heavy calculations.
  
      Parameters
      ----------
      x
          The original matrix whose condition number is to be determined.
      cond_limit
          The greater the condition number, the more ill-conditioned the matrix
          will be, the more it will be prone to numerical instabilities.
  
          There is no rule of thumb for what the exact condition number
          should be to consider a matrix ill-conditioned(prone to numerical errors).
          But, if the condition number is "1", the matrix is perfectly said to be a
          well-conditioned matrix which will not be prone to any type of numerical
          instabilities in further calculations, but that would probably be a
          very simple matrix.
  
          The cond_limit should start with "30", gradually decreasing it according
          to our use, lower cond_limit would result in more numerically stable
          matrices but more simple matrices.
  
          The limit should always be in the range "1-30", greater the number greater
          the computational instability. Should not increase 30, it leads to strong
          multi-collinearity which leads to singularity.
  
      Returns
      -------
      ret
          If True, the matrix is suitable for further numerical computations.
      """
      return np.all(np.linalg.cond(x.astype("float64")) <= cond_limit)
  
imports: from hypothesis import assume, strategies
  from typing import Tuple
  from functools import lru_cache
  import math
  import numpy
  import ivy
  from .None import array_helpers, number_helpers, dtype_helpers
  from ..pipeline_helper import WithBackendContext
  from ivy.functional.ivy.layers import _deconv_length
  from ..globals import mod_backend
label_desc: Use image node_id 1 for calling a global function with example usage: matrix_is_stable(x, cond_limit) and returns: np