parameters: (shape,
                                 seed,
                                 mean=0.0,
                                 stddev=1.0,
                                 dtype=dtypes.float32,
                                 name=None)
docstring: Outputs deterministic pseudorandom values, truncated normally distributed.
  
    This is a stateless version of `tf.random.truncated_normal`: if run twice with
    the
    same seeds, it will produce the same pseudorandom numbers.  The output is
    consistent across multiple runs on the same hardware (and between CPU
    and GPU), but may change between versions of TensorFlow or on non-CPU/GPU
    hardware.
  
    The generated values follow a normal distribution with specified mean and
    standard deviation, except that values whose magnitude is more than 2 standard
    deviations from the mean are dropped and re-picked.
  
    Args:
      shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
      seed: A shape [2] integer Tensor of seeds to the random number generator.
      mean: A 0-D Tensor or Python value of type `dtype`. The mean of the
        truncated normal distribution.
      stddev: A 0-D Tensor or Python value of type `dtype`. The standard deviation
        of the normal distribution, before truncation.
      dtype: The type of the output.
      name: A name for the operation (optional).
  
    Returns:
      A tensor of the specified shape filled with random truncated normal values.
docstring_summary: Outputs deterministic pseudorandom values, truncated normally distributed.
docstring_tokens: Outputs deterministic pseudorandom values truncated normally distributed .
function: def stateless_truncated_normal(shape,
                                 seed,
                                 mean=0.0,
                                 stddev=1.0,
                                 dtype=dtypes.float32,
                                 name=None):
    """Outputs deterministic pseudorandom values, truncated normally distributed.
  
    This is a stateless version of `tf.random.truncated_normal`: if run twice with
    the
    same seeds, it will produce the same pseudorandom numbers.  The output is
    consistent across multiple runs on the same hardware (and between CPU
    and GPU), but may change between versions of TensorFlow or on non-CPU/GPU
    hardware.
  
    The generated values follow a normal distribution with specified mean and
    standard deviation, except that values whose magnitude is more than 2 standard
    deviations from the mean are dropped and re-picked.
  
    Args:
      shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
      seed: A shape [2] integer Tensor of seeds to the random number generator.
      mean: A 0-D Tensor or Python value of type `dtype`. The mean of the
        truncated normal distribution.
      stddev: A 0-D Tensor or Python value of type `dtype`. The standard deviation
        of the normal distribution, before truncation.
      dtype: The type of the output.
      name: A name for the operation (optional).
  
    Returns:
      A tensor of the specified shape filled with random truncated normal values.
    """
    with ops.name_scope(name, "stateless_truncated_normal",
                        [shape, seed, mean, stddev]) as name:
      shape = tensor_util.shape_tensor(shape)
      mean = ops.convert_to_tensor(mean, dtype=dtype, name="mean")
      stddev = ops.convert_to_tensor(stddev, dtype=dtype, name="stddev")
      rnd = gen_stateless_random_ops.stateless_truncated_normal(
          shape, seed, dtype)
      result = math_ops.add(rnd * stddev, mean, name=name)
      tensor_util.maybe_set_static_shape(result, shape)
      return result
function_tokens: def stateless_truncated_normal ( shape , seed , mean = 0.0 , stddev = 1.0 , dtype = dtypes . float32 , name = None ) : with ops . name_scope ( name , "stateless_truncated_normal" , [ shape , seed , mean , stddev ] ) as name : shape = tensor_util . shape_tensor ( shape ) mean = ops . convert_to_tensor ( mean , dtype = dtype , name = "mean" ) stddev = ops . convert_to_tensor ( stddev , dtype = dtype , name = "stddev" ) rnd = gen_stateless_random_ops . stateless_truncated_normal ( shape , seed , dtype ) result = math_ops . add ( rnd * stddev , mean , name = name ) tensor_util . maybe_set_static_shape ( result , shape ) return result