class_docstr: Bijector constructed from custom callables.
  
  Example Use:
  
  ```python
  exp = Inline(
      forward_fn=tf.exp,
      inverse_fn=tf.math.log,
      inverse_log_det_jacobian_fn=lambda y: -tf.math.log(y),
      forward_min_event_ndims=0,
      is_increasing=True,
      name='exp')
  ```
  
  The above example is equivalent to the `Bijector` `Exp()`.
docstr: Creates a `Bijector` from callables.
  
  At the minimum, you must supply one of `forward_min_event_ndims` or
  `inverse_min_event_ndims`. To be fully functional, a typical bijector will
  also require `forward_fn`, `inverse_fn` and at least one of
  `inverse_log_det_jacobian_fn` or `forward_log_det_jacobian_fn`.
  
  Args:
    forward_fn: Python callable implementing the forward transformation.
    inverse_fn: Python callable implementing the inverse transformation.
    inverse_log_det_jacobian_fn: Python callable implementing the
      `log o det o jacobian` of the inverse transformation.
    forward_log_det_jacobian_fn: Python callable implementing the
      `log o det o jacobian` of the forward transformation.
    forward_event_shape_fn: Python callable implementing non-identical
      static event shape changes. Default: shape is assumed unchanged.
    forward_event_shape_tensor_fn: Python callable implementing non-identical
      event shape changes. Default: shape is assumed unchanged.
    inverse_event_shape_fn: Python callable implementing non-identical
      static event shape changes. Default: shape is assumed unchanged.
    inverse_event_shape_tensor_fn: Python callable implementing non-identical
      event shape changes. Default: shape is assumed unchanged.
    is_constant_jacobian: Python `bool` indicating that the Jacobian is
      constant for all input arguments.
    is_increasing: `bool` `Tensor` indicating a scalar bijector function is
      increasing for all input arguments, or a callable returning a `bool`
      `Tensor` specifying such truth values.
    validate_args: Python `bool` indicating whether arguments should be
      checked for correctness.
    forward_min_event_ndims: Python `int` indicating the minimal
      dimensionality this bijector acts on.
    inverse_min_event_ndims: Python `int` indicating the minimal
      dimensionality this bijector acts on.
    name: Python `str`, name given to ops managed by this object.
  
  Raises:
    TypeError: If any of the non-`None` `*_fn` arguments are not callable.
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code: def __init__(
      self,
      forward_fn=None,
      inverse_fn=None,
      inverse_log_det_jacobian_fn=None,
      forward_log_det_jacobian_fn=None,
      forward_event_shape_fn=None,
      forward_event_shape_tensor_fn=None,
      inverse_event_shape_fn=None,
      inverse_event_shape_tensor_fn=None,
      is_constant_jacobian=False,
      is_increasing=None,
      validate_args=False,
      forward_min_event_ndims=bijector.UNSPECIFIED,
      inverse_min_event_ndims=bijector.UNSPECIFIED,
      name="inline",
  ):
      """Creates a `Bijector` from callables.
  
      At the minimum, you must supply one of `forward_min_event_ndims` or
      `inverse_min_event_ndims`. To be fully functional, a typical bijector will
      also require `forward_fn`, `inverse_fn` and at least one of
      `inverse_log_det_jacobian_fn` or `forward_log_det_jacobian_fn`.
  
      Args:
        forward_fn: Python callable implementing the forward transformation.
        inverse_fn: Python callable implementing the inverse transformation.
        inverse_log_det_jacobian_fn: Python callable implementing the
          `log o det o jacobian` of the inverse transformation.
        forward_log_det_jacobian_fn: Python callable implementing the
          `log o det o jacobian` of the forward transformation.
        forward_event_shape_fn: Python callable implementing non-identical
          static event shape changes. Default: shape is assumed unchanged.
        forward_event_shape_tensor_fn: Python callable implementing non-identical
          event shape changes. Default: shape is assumed unchanged.
        inverse_event_shape_fn: Python callable implementing non-identical
          static event shape changes. Default: shape is assumed unchanged.
        inverse_event_shape_tensor_fn: Python callable implementing non-identical
          event shape changes. Default: shape is assumed unchanged.
        is_constant_jacobian: Python `bool` indicating that the Jacobian is
          constant for all input arguments.
        is_increasing: `bool` `Tensor` indicating a scalar bijector function is
          increasing for all input arguments, or a callable returning a `bool`
          `Tensor` specifying such truth values.
        validate_args: Python `bool` indicating whether arguments should be
          checked for correctness.
        forward_min_event_ndims: Python `int` indicating the minimal
          dimensionality this bijector acts on.
        inverse_min_event_ndims: Python `int` indicating the minimal
          dimensionality this bijector acts on.
        name: Python `str`, name given to ops managed by this object.
  
      Raises:
        TypeError: If any of the non-`None` `*_fn` arguments are not callable.
      """
      parameters = dict(locals())
      with tf.name_scope(name) as name:
          self._maybe_implement(forward_fn, "_forward", "forward_fn")
          self._maybe_implement(inverse_fn, "_inverse", "inverse_fn")
          self._maybe_implement(
              inverse_log_det_jacobian_fn,
              "_inverse_log_det_jacobian",
              "inverse_log_det_jacobian_fn",
          )
          self._maybe_implement(
              forward_log_det_jacobian_fn,
              "_forward_log_det_jacobian",
              "forward_log_det_jacobian_fn",
          )
          if is_increasing is not None and not callable(is_increasing):
              is_increasing_val = is_increasing
              is_increasing = lambda: is_increasing_val
          self._maybe_implement(
              is_increasing, "_is_increasing", "is_increasing"
          )
  
          # By default assume shape doesn't change.
          self._forward_event_shape = _maybe_impute_as_identity(
              forward_event_shape_fn, "forward_event_shape_fn"
          )
          self._forward_event_shape_tensor = _maybe_impute_as_identity(
              forward_event_shape_tensor_fn,
              "forward_event_shape_tensor_fn",
          )
          self._inverse_event_shape = _maybe_impute_as_identity(
              inverse_event_shape_fn, "inverse_event_shape_fn"
          )
          self._inverse_event_shape_tensor = _maybe_impute_as_identity(
              inverse_event_shape_tensor_fn,
              "inverse_event_shape_tensor_fn",
          )
  
          super(Inline, self).__init__(
              forward_min_event_ndims=forward_min_event_ndims,
              inverse_min_event_ndims=inverse_min_event_ndims,
              is_constant_jacobian=is_constant_jacobian,
              validate_args=validate_args,
              parameters=parameters,
              name=name,
          )
  
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
label_desc: Use image node_id 1 to create a new Inline object from inherited base classes: bijector with example: obj = Inline(forward_fn, inverse_fn, inverse_log_det_jacobian_fn, forward_log_det_jacobian_fn, forward_event_shape_fn, forward_event_shape_tensor_fn, inverse_event_shape_fn, inverse_event_shape_tensor_fn, is_constant_jacobian, is_increasing, validate_args, forward_min_event_ndims, inverse_min_event_ndims, name)