class_docstr: A bijector that clips by value.
  
  This class is intended for minute numerical issues where `|clip(x) - x| <=
  eps`, as it defines the derivative of its application to be exactly 1.
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docstr: Instantiates the `ClipByValue` bijector.
  
  Args:
    clip_value_min: Floating-point `Tensor`.
    clip_value_max: Floating-point `Tensor`.
    validate_args: Python `bool` indicating whether arguments should be
      checked for correctness.
    name: Python `str` name given to ops managed by this object.
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code: def __init__(
      self,
      clip_value_min,
      clip_value_max,
      validate_args=False,
      name="clip_by_value",
  ):
      """Instantiates the `ClipByValue` bijector.
  
      Args:
        clip_value_min: Floating-point `Tensor`.
        clip_value_max: Floating-point `Tensor`.
        validate_args: Python `bool` indicating whether arguments should be
          checked for correctness.
        name: Python `str` name given to ops managed by this object.
      """
      parameters = dict(locals())
      with tf.name_scope(name) as name:
          dtype = dtype_util.common_dtype(
              [clip_value_min, clip_value_max], dtype_hint=tf.float32
          )
          self._clip_value_min = tensor_util.convert_nonref_to_tensor(
              clip_value_min, dtype=dtype, name="clip_value_min"
          )
          self._clip_value_max = tensor_util.convert_nonref_to_tensor(
              clip_value_max, dtype=dtype, name="clip_value_max"
          )
          super(_ClipByValue, self).__init__(
              forward_min_event_ndims=0,
              is_constant_jacobian=True,
              dtype=dtype,
              validate_args=validate_args,
              parameters=parameters,
              name=name,
          )
  
imports: import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
  from tensorflow_probability.python.bijectors import chain
  from tensorflow_probability.python.bijectors import cholesky_outer_product
  from tensorflow_probability.python.bijectors import correlation_cholesky
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import beta
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  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.math import linalg
  from tensorflow_probability.python.math import special
  from tensorflow_probability.python.math.numeric import clip_by_value_preserve_gradient
  from tensorflow_probability.python.random import random_ops
  from tensorflow.python.ops import control_flow_util
label_desc: Use image node_id 1 to create a new _ClipByValue object from inherited base classes: bijector_lib with example: obj = _ClipByValue(clip_value_min, clip_value_max, validate_args, name)