class_docstr: Convenience object for capturing variables closed over by Inline.
class_docstr_tok: ["Convenience","object","for","capturing","variables","closed","over","by","Inline","."]
code: def __init__(self, fn, varobj):
      self._fn = fn
      self._varobj = varobj
  
imports: import collections
  import functools
  from absl.testing import parameterized
  import hypothesis
  from hypothesis import strategies
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python import bijectors
  from tensorflow_probability.python import util
  from tensorflow_probability.python.bijectors import hypothesis_testlib
  from tensorflow_probability.python.bijectors import invert
  from tensorflow_probability.python.internal import auto_composite_tensor
  from tensorflow_probability.python.internal import hypothesis_testlib
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow.python.util import nest
label_desc: Use image node_id 1 to create a new CallableModule object from inherited base classes: tf, auto_composite_tensor with example: obj = CallableModule(fn, varobj)