parameters: (self,
                 batch_ndims=None,
                 event_ndims=None,
                 validate_args=False,
                 name="DistributionShape")
docstring: Construct `DistributionShape` with fixed `batch_ndims`, `event_ndims`.
  
      `batch_ndims` and `event_ndims` are fixed throughout the lifetime of a
      `Distribution`.  They may only be known at graph execution.
  
      If both `batch_ndims` and `event_ndims` are python scalars (rather than
      either being a `Tensor`), functions in this class automatically perform
      sanity checks during graph construction.
  
      Args:
        batch_ndims: `Tensor`. 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: `Tensor`. 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].
        validate_args: `Boolean`, default `False`. When `True`, non-`tf.constant`
          `Tensor` arguments are checked for correctness. (`tf.constant`
          arguments are always checked.)
        name: `String`. The name prepended to Ops created by this class.
  
      Raises:
        ValueError: if either `batch_ndims` or `event_ndims` are: `None`,
          negative, not `int32`.
docstring_summary: Construct `DistributionShape` with fixed `batch_ndims`, `event_ndims`.
docstring_tokens: Construct DistributionShape with fixed batch_ndims event_ndims .
function: def __init__(self,
                 batch_ndims=None,
                 event_ndims=None,
                 validate_args=False,
                 name="DistributionShape"):
      """Construct `DistributionShape` with fixed `batch_ndims`, `event_ndims`.
  
      `batch_ndims` and `event_ndims` are fixed throughout the lifetime of a
      `Distribution`.  They may only be known at graph execution.
  
      If both `batch_ndims` and `event_ndims` are python scalars (rather than
      either being a `Tensor`), functions in this class automatically perform
      sanity checks during graph construction.
  
      Args:
        batch_ndims: `Tensor`. 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: `Tensor`. 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].
        validate_args: `Boolean`, default `False`. When `True`, non-`tf.constant`
          `Tensor` arguments are checked for correctness. (`tf.constant`
          arguments are always checked.)
        name: `String`. The name prepended to Ops created by this class.
  
      Raises:
        ValueError: if either `batch_ndims` or `event_ndims` are: `None`,
          negative, not `int32`.
      """
      if batch_ndims is None: raise ValueError("batch_ndims cannot be None")
      if event_ndims is None: raise ValueError("event_ndims cannot be None")
      self._batch_ndims = batch_ndims
      self._event_ndims = event_ndims
      self._validate_args = validate_args
      with ops.name_scope(name) as ns:
        self._name = ns
        with ops.name_scope("init"):
          self._batch_ndims = self._assert_non_negative_int32_scalar(
              ops.convert_to_tensor(
                  batch_ndims, name="batch_ndims"))
          self._batch_ndims_static, self._batch_ndims_is_0 = (
              self._introspect_ndims(self._batch_ndims))
          self._event_ndims = self._assert_non_negative_int32_scalar(
              ops.convert_to_tensor(
                  event_ndims, name="event_ndims"))
          self._event_ndims_static, self._event_ndims_is_0 = (
              self._introspect_ndims(self._event_ndims))
function_tokens: def __init__ ( self , batch_ndims = None , event_ndims = None , validate_args = False , name = "DistributionShape" ) : if batch_ndims is None : raise ValueError ( "batch_ndims cannot be None" ) if event_ndims is None : raise ValueError ( "event_ndims cannot be None" ) self . _batch_ndims = batch_ndims self . _event_ndims = event_ndims self . _validate_args = validate_args with ops . name_scope ( name ) as ns : self . _name = ns with ops . name_scope ( "init" ) : self . _batch_ndims = self . _assert_non_negative_int32_scalar ( ops . convert_to_tensor ( batch_ndims , name = "batch_ndims" ) ) self . _batch_ndims_static , self . _batch_ndims_is_0 = ( self . _introspect_ndims ( self . _batch_ndims ) ) self . _event_ndims = self . _assert_non_negative_int32_scalar ( ops . convert_to_tensor ( event_ndims , name = "event_ndims" ) ) self . _event_ndims_static , self . _event_ndims_is_0 = ( self . _introspect_ndims ( self . _event_ndims ) )