code: def lower_bound(
      inputs, bound, gradient="identity_if_towards", name="lower_bound"
  ):
      """Same as `tf.maximum`, but with helpful gradient for `inputs < bound`.
  
      This function behaves just like `tf.maximum`, but the behavior of the gradient
      with respect to `inputs` for input values that hit the bound depends on
      `gradient`:
  
      If set to `'disconnected'`, the returned gradient is zero for values that hit
      the bound. This is identical to the behavior of `tf.maximum`.
  
      If set to `'identity'`, the gradient is unconditionally replaced with the
      identity function (i.e., pretending this function does not exist).
  
      If set to `'identity_if_towards'`, the gradient is replaced with the identity
      function, but only if applying gradient descent would push the values of
      `inputs` towards the bound. For gradient values that push away from the bound,
      the returned gradient is still zero.
  
      Note: In the latter two cases, no gradient is returned for `bound`.
      Also, the implementation of `gradient == 'identity_if_towards'` currently
      assumes that the shape of `inputs` is the same as the shape of the output. It
      won't work reliably for all possible broadcasting scenarios.
  
      Args:
        inputs: Input tensor.
        bound: Lower bound for the input tensor.
        gradient: 'disconnected', 'identity', or 'identity_if_towards' (default).
        name: Name for this op.
  
      Returns:
        `tf.maximum(inputs, bound)`
  
      Raises:
        ValueError: for invalid value of `gradient`.
      """
      with tf.name_scope(name) as scope:
          inputs = tf.convert_to_tensor(inputs, name="inputs")
          bound = tf.convert_to_tensor(
              bound, name="bound", dtype=inputs.dtype
          )
  
          def identity_if_towards_grad(grad):
              """Gradient if gradient == 'identity_if_towards'."""
              pass_through_if = tf.logical_or(inputs >= bound, grad < 0)
              return (tf.cast(pass_through_if, grad.dtype) * grad, None)
  
          def disconnected_grad(grad):
              """Gradient if gradient == 'disconnected'."""
              return (tf.cast(inputs >= bound, grad.dtype) * grad, None)
  
          try:
              gradient = {
                  "identity_if_towards": identity_if_towards_grad,
                  "identity": lambda grad: (grad, None),
                  "disconnected": disconnected_grad,
              }[gradient]
          except KeyError:
              raise ValueError(
                  "Invalid value for `gradient`: '{}'.".format(gradient)
              )
  
          @tf.custom_gradient
          def _lower_bound(inputs, bound):
              return tf.maximum(inputs, bound, name=scope), gradient
  
          return _lower_bound(inputs, bound)
  
label_desc: Use image node_id 2 for calling a global function with example usage: lower_bound(inputs, bound, gradient, name) and returns: _lower_bound, unknown,  None, unknown,  None, tf,  gradient