code: def show():
      """
      Show libraries in the system on which NumPy was built.
  
      Print information about various resources (libraries, library
      directories, include directories, etc.) in the system on which
      NumPy was built.
  
      See Also
      --------
      get_include : Returns the directory containing NumPy C
                    header files.
  
      Notes
      -----
      1. Classes specifying the information to be printed are defined
         in the `numpy.distutils.system_info` module.
  
         Information may include:
  
         * ``language``: language used to write the libraries (mostly
           C or f77)
         * ``libraries``: names of libraries found in the system
         * ``library_dirs``: directories containing the libraries
         * ``include_dirs``: directories containing library header files
         * ``src_dirs``: directories containing library source files
         * ``define_macros``: preprocessor macros used by
           ``distutils.setup``
         * ``baseline``: minimum CPU features required
         * ``found``: dispatched features supported in the system
         * ``not found``: dispatched features that are not supported
           in the system
  
      2. NumPy BLAS/LAPACK Installation Notes
  
         Installing a numpy wheel (``pip install numpy`` or force it
         via ``pip install numpy --only-binary :numpy: numpy``) includes
         an OpenBLAS implementation of the BLAS and LAPACK linear algebra
         APIs. In this case, ``library_dirs`` reports the original build
         time configuration as compiled with gcc/gfortran; at run time
         the OpenBLAS library is in
         ``site-packages/numpy.libs/`` (linux), or
         ``site-packages/numpy/.dylibs/`` (macOS), or
         ``site-packages/numpy/.libs/`` (windows).
  
         Installing numpy from source
         (``pip install numpy --no-binary numpy``) searches for BLAS and
         LAPACK dynamic link libraries at build time as influenced by
         environment variables NPY_BLAS_LIBS, NPY_CBLAS_LIBS, and
         NPY_LAPACK_LIBS; or NPY_BLAS_ORDER and NPY_LAPACK_ORDER;
         or the optional file ``~/.numpy-site.cfg``.
         NumPy remembers those locations and expects to load the same
         libraries at run-time.
         In NumPy 1.21+ on macOS, 'accelerate' (Apple's Accelerate BLAS
         library) is in the default build-time search order after
         'openblas'.
  
      Examples
      --------
      >>> import numpy as np
      >>> np.show_config()
      blas_opt_info:
          language = c
          define_macros = [('HAVE_CBLAS', None)]
          libraries = ['openblas', 'openblas']
          library_dirs = ['/usr/local/lib']
      """
      from numpy.core._multiarray_umath import (
          __cpu_features__,
          __cpu_baseline__,
          __cpu_dispatch__,
      )
  
      for name, info_dict in globals().items():
          if name[0] == "_" or type(info_dict) is not type({}):
              continue
          print(name + ":")
          if not info_dict:
              print("  NOT AVAILABLE")
          for k, v in info_dict.items():
              v = str(v)
              if k == "sources" and len(v) > 200:
                  v = v[:60] + " ...\n... " + v[-60:]
              print("    %s = %s" % (k, v))
  
      features_found, features_not_found = [], []
      for feature in __cpu_dispatch__:
          if __cpu_features__[feature]:
              features_found.append(feature)
          else:
              features_not_found.append(feature)
  
      print("Supported SIMD extensions in this NumPy install:")
      print("    baseline = %s" % (",".join(__cpu_baseline__)))
      print("    found = %s" % (",".join(features_found)))
      print("    not found = %s" % (",".join(features_not_found)))
  
imports: import os
  import sys
label_desc: Use image node_id 2 for calling a global function with example usage: show() without return types