code: def bessel_ive(v, z, name=None):
      """Computes exponentially scaled modified Bessel function of the first kind.
  
      This function computes `Ive`, which is an exponentially scaled version
      of the modified Bessel function of the first kind.
  
      `Ive(v, z) = Iv(v, z) * exp(-abs(z))`
  
      Warning: Gradients with respect to the first parameter `v` are currently not
      defined.
  
      Args:
        v: Floating-point `Tensor` broadcastable with `z` for which `Ive(v, z)`
          should be computed. `v` is expected to be non-negative.
        z: Floating-point `Tensor` broadcastable with `v` for which `Ive(v, z)`
          should be computed. If `z` is negative, `v` is expected to be an integer.
        name: A name for the operation (optional).
          Default value: `None` (i.e., 'bessel_ive').
  
      Returns:
        bessel_ive: Exponentially modified Bessel Function of the first kind.
      """
      with tf.name_scope(name or "bessel_ive"):
          dtype = dtype_util.common_dtype([v, z], tf.float32)
          v = tf.convert_to_tensor(v, dtype=dtype)
          z = tf.convert_to_tensor(z, dtype=dtype)
          return _bessel_ive_custom_gradient(v, z)
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import custom_gradient
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.math import generic
label_desc: Use image node_id 21 for calling a global function with example usage: bessel_ive(v, z, name) and returns: _bessel_ive_custom_gradient