code: def _instantiable_base_kernels():
      """Computes the table of mechanically instantiable base Kernels.
  
      A Kernel is mechanically instantiable if
  
      - The class appears as a symbol binding in `tfp.math.psd_kernels`;
      - The class defines a `_params_event_ndims` method (necessary
        to generate parameter Tensors with predictable batch shapes); and
      - The name is not blocklisted in `SPECIAL_KERNELS`.
  
      Compound kernels have their own
      instantiation rules hard-coded in the `kernel` strategy.
  
      Returns:
        instantiable_base_kernels: A Python dict mapping kernel name
          (as a string) to a `KernelInfo` carrying the information necessary to
          instantiate it.
      """
      result = {}
      for kernel_name in dir(tfpk):
          kernel_class = getattr(tfpk, kernel_name)
          if (
              not inspect.isclass(kernel_class)
              or not issubclass(
                  kernel_class, tfpk.PositiveSemidefiniteKernel
              )
              or kernel_name in SPECIAL_KERNELS
              or kernel_name in NON_INSTANTIABLE_SPECIAL_KERNELS
          ):
              continue
          try:
              params_event_ndims = {
                  k: p.event_ndims
                  for (
                      k,
                      p,
                  ) in kernel_class.parameter_properties().items()
                  if p.is_tensor and p.event_ndims is not None
              }
          except NotImplementedError:
              logging.warning(
                  "Unable to test tfd.%s: `parameter_properties()` is not "
                  "implemented or does not define concrete (integer) `event_ndims` "
                  "for all parameters.",
                  kernel_name,
              )
          result[kernel_name] = KernelInfo(
              kernel_class, params_event_ndims
          )
  
      return result
  
imports: import collections
  import contextlib
  import inspect
  import logging
  import re
  import hypothesis
  from hypothesis.extra import numpy
  import hypothesis.strategies
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.experimental.distributions import marginal_fns
  from tensorflow_probability.python.internal import hypothesis_testlib
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.math import psd_kernels
label_desc: Use image node_id 1 for calling a global function with example usage: _instantiable_base_kernels() and returns: result