class_docstr: A running variance computation.
  
  This is just an alias for `RunningCovariance`, with the `event_ndims` set to 0
  to compute variances.
  
  `RunningVariance` is meant to serve general streaming variance needs.
  For a specialized version that fits streaming over MCMC samples, see
  `VarianceReducer` in `tfp.experimental.mcmc`.
docstr: Starts a `RunningVariance` from shape and dtype metadata.
  
  Args:
    shape: Python `Tuple` or `TensorShape` representing the shape of incoming
      samples.  This is useful to supply if the `RunningVariance` will be
      carried by a `tf.while_loop`, so that broadcasting does not change the
      shape across loop iterations.
    dtype: Dtype of incoming samples and the resulting statistics.
      By default, the dtype is `tf.float32`. Any integer dtypes will be
      cast to corresponding floats (i.e. `tf.int32` will be cast to
      `tf.float32`), as intermediate calculations should be performing
      floating-point division.
  
  Returns:
    var: An empty `RunningCovariance`, ready for incoming samples.
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code: def from_shape(cls, shape=(), dtype=tf.float32):
      """Starts a `RunningVariance` from shape and dtype metadata.
  
      Args:
        shape: Python `Tuple` or `TensorShape` representing the shape of incoming
          samples.  This is useful to supply if the `RunningVariance` will be
          carried by a `tf.while_loop`, so that broadcasting does not change the
          shape across loop iterations.
        dtype: Dtype of incoming samples and the resulting statistics.
          By default, the dtype is `tf.float32`. Any integer dtypes will be
          cast to corresponding floats (i.e. `tf.int32` will be cast to
          `tf.float32`), as intermediate calculations should be performing
          floating-point division.
  
      Returns:
        var: An empty `RunningCovariance`, ready for incoming samples.
      """
      return super().from_shape(shape, dtype, event_ndims=0)
  
imports: import functools
  import math
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import nest_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.mcmc import diagnostic
  from tensorflow.python.util import nest
label_desc: Use image node_id 1 for calling the RunningVariance obj's underlying member method code with example usage: obj.from_shape(cls, shape, dtype) and returns: super