parameters: (self, batch_ndims=None, event_ndims=None, name='ShapeUtil')
docstring: Construct ShapeUtil with known sample, batch, and/or event ndims.
  
      Typically, batch_ndims and event_ndims are fixed throughout the lifetime of
      a Distribution.
  
      Args:
        batch_ndims: number of dims (rank) of the batch portion of indexes of a
          `Tensor`.  A "batch" is a non-identical distribution, i.e, Normal with
          different parameters.
        event_ndims: number of dims (rank) of the event portion of indexes of a
          `Tensor`. An "event" is what is sampled from a distribution, i.e., a
          trivariate Normal has an event shape of [3] and a 4 dimensional Wishart
          has an event shape of [4, 4].
        name: `String`. The name to give Ops created by this class.
  
      Raises:
        ValueError: if batch_ndims or event_ndims are invalid.
docstring_summary: Construct ShapeUtil with known sample, batch, and/or event ndims.
docstring_tokens: Construct ShapeUtil with known sample batch and / or event ndims .
function: def __init__(self, batch_ndims=None, event_ndims=None, name='ShapeUtil'):
      """Construct ShapeUtil with known sample, batch, and/or event ndims.
  
      Typically, batch_ndims and event_ndims are fixed throughout the lifetime of
      a Distribution.
  
      Args:
        batch_ndims: number of dims (rank) of the batch portion of indexes of a
          `Tensor`.  A "batch" is a non-identical distribution, i.e, Normal with
          different parameters.
        event_ndims: number of dims (rank) of the event portion of indexes of a
          `Tensor`. An "event" is what is sampled from a distribution, i.e., a
          trivariate Normal has an event shape of [3] and a 4 dimensional Wishart
          has an event shape of [4, 4].
        name: `String`. The name to give Ops created by this class.
  
      Raises:
        ValueError: if batch_ndims or event_ndims are invalid.
      """
      if batch_ndims < 0:
        raise ValueError('must specify non-negative batch_ndims(%d)', batch_ndims)
      if batch_ndims > 0 and event_ndims < 1:
        raise ValueError('must specify positive event_ndims(%d) when '
                         'batch_ndims(%d) is positive', event_ndims, batch_ndims)
      # TODO(jvdillon): Support batches of scalars.
      self._name = name
      self._batch_ndims = batch_ndims
      self._event_ndims = event_ndims
function_tokens: def __init__ ( self , batch_ndims = None , event_ndims = None , name = 'ShapeUtil' ) : if batch_ndims < 0 : raise ValueError ( 'must specify non-negative batch_ndims(%d)' , batch_ndims ) if batch_ndims > 0 and event_ndims < 1 : raise ValueError ( 'must specify positive event_ndims(%d) when ' 'batch_ndims(%d) is positive' , event_ndims , batch_ndims ) # TODO(jvdillon): Support batches of scalars. self . _name = name self . _batch_ndims = batch_ndims self . _event_ndims = event_ndims