code: def _truncated_normal_jax(
      shape,
      seed,
      means=0.0,
      stddevs=1.0,
      minvals=-2.0,
      maxvals=2.0,
      name=None,
  ):  # pylint: disable=unused-argument
      import jax.random as jaxrand  # pylint: disable=g-import-not-at-top
  
      if seed is None:
          raise ValueError("Must provide PRNGKey to sample in JAX.")
      dtype = utils.common_dtype([means, stddevs, minvals, maxvals])
      std_low = (minvals - means) / stddevs
      std_high = (maxvals - means) / stddevs
      std_samps = jaxrand.truncated_normal(
          seed, std_low, std_high, shape, dtype
      )
      return std_samps * stddevs + means
  
imports: import numpy
  import numpy
  from tensorflow_probability.python.internal.backend.numpy import _utils
  from tensorflow_probability.python.internal.backend.numpy import ops
  from tensorflow_probability.python.internal.backend.numpy.numpy_math import softmax
label_desc: Use image node_id 15 for calling a global function with example usage: _truncated_normal_jax(shape, seed, means, stddevs, minvals, maxvals, name) and returns: unknown