code: def _truncated_normal(
      shape,
      seed,
      means=0.0,
      stddevs=1.0,
      minvals=-2.0,
      maxvals=2.0,
      name=None,
  ):  # pylint: disable=unused-argument
      from scipy import stats  # pylint: disable=g-import-not-at-top
  
      rng = (
          np.random
          if seed is None
          else np.random.RandomState(seed & 0xFFFFFFFF)
      )
      std_low = (minvals - means) / stddevs
      std_high = (maxvals - means) / stddevs
      std_samps = stats.truncnorm.rvs(
          std_low, std_high, size=shape, random_state=rng
      )
      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 14 for calling a global function with example usage: _truncated_normal(shape, seed, means, stddevs, minvals, maxvals, name) and returns: unknown