code: def observe(M, C, obs_mesh, obs_vals, obs_V=0, lintrans=None,
              cross_validate=True):
      
      obs_mesh = regularize_array(obs_mesh)
      # print_(obs_mesh)
      obs_V = resize(obs_V, obs_mesh.shape[0])
      obs_vals = resize(obs_vals, obs_mesh.shape[0])
  
      # First observe C.
      relevant_slice, obs_mesh_new = C.observe(obs_mesh, obs_V, output_type='o')
  
      # Then observe M from C.
      M.observe(C, obs_mesh_new, obs_vals.ravel()[relevant_slice])
  
      # Cross-validate if not asked not to.
      if obs_mesh_new.shape[0] < obs_mesh.shape[0]:
          if cross_validate:
              if not predictive_check(
                      obs_vals, obs_mesh, M, C.obs_piv, sqrt(C.relative_precision)):
                  raise ValueError(
                      "These data seem extremely improbable given your GP prior. \n Suggestions: decrease observation precision, or adjust the covariance to \n allow the function to be less smooth.")
docs: (M, C, obs_mesh, obs_vals[, obs_V = 0, lintrans = None, cross_validate = True])
  
  
      Imposes observation of the value of obs_vals on M and C, where
  
      obs_vals ~ N(lintrans * f(obs_mesh), V)
      f ~ GP(M,C)
  
  
      :Arguments:
  
          -   `M`: The mean function
  
          -   `C`: The covariance function
  
          -   `obs_mesh`: The places where f has been evaluated.
  
          -   `obs_vals`: The values of f that were observed there.
  
          -   `obs_V`: The observation variance. If None, assumed to be infinite
              (observations made with no error).
  
          -   `lintrans`: A linear transformation. If None, assumed to be the
              identity transformation (pretend it doesn't exist).
  
          -   `cross_validate`: A flag indicating whether a check should be done to
              see if the data could have arisen from M and C with positive probability.