class_docstr: Split a `Tensor` event along an axis into a list of `Tensor`s.
  
  Example Use:
  
  ```python
  split = tfb.Split(
      num_or_size_splits=[4, 1, 3],
      axis=-1
    )
  y = split.forward(tf.zeros([5, 6, 8]))
  ==> [<`Tensor`, shape=(5, 6, 4)>,
       <`Tensor`, shape=(5, 6, 1)>,
       <`Tensor`, shape=(5, 6, 3)>]
  
  # The inverse of `split` concatenates a list of `Tensor`s along `axis`.
  x_ = split.inverse(y)
  x_.shape
  ==> TensorShape([5, 6, 8])
  ```
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docstr: Shape of a single sample from a single batch as an `int32` 1D `Tensor`.
  
  Args:
    output_shapes: An iterable of `Tensor`, `int32` vectors indicating
      event-shapes passed into `inverse` function. The length of the iterable
      must be equal to the number of splits.
  
  Returns:
    inverse_event_shape_tensor: `Tensor`, `int32` vector indicating
      event-portion shape after applying `inverse`.
docstr_tok: ["Shape","of","a","single","sample","from","a","single","batch","as","an","`","int32","`","1D","`","Tensor","`",".","Args",":","output_shapes",":","An","iterable","of","`","Tensor","`",",","`","int32","`","vectors","indicating","event-shapes","passed","into","`","inverse","`","function",".","The","length","of","the","iterable","must","be","equal","to","the","number","of","splits",".","Returns",":","inverse_event_shape_tensor",":","`","Tensor","`",",","`","int32","`","vector","indicating","event-portion","shape","after","applying","`","inverse","`","."]
code: def _inverse_event_shape_tensor(self, output_shapes):
      """Shape of a single sample from a single batch as an `int32` 1D `Tensor`.
  
      Args:
        output_shapes: An iterable of `Tensor`, `int32` vectors indicating
          event-shapes passed into `inverse` function. The length of the iterable
          must be equal to the number of splits.
  
      Returns:
        inverse_event_shape_tensor: `Tensor`, `int32` vector indicating
          event-portion shape after applying `inverse`.
      """
      # Validate `output_shapes` statically if possible and get assertions.
      is_validated = self._validate_output_shapes(
          [
              tensorshape_util.constant_value_as_shape(s)
              for s in output_shapes
          ]
      )
      if is_validated or not self.validate_args:
          assertions = []
      else:
          assertions = self._validate_output_shape_tensors(
              output_shapes
          )
  
      with tf.control_dependencies(assertions):
          total_size = tf.reduce_sum(
              [t[self.axis] for t in output_shapes]
          )
          inverse_event_shape = tf.tensor_scatter_nd_update(
              output_shapes[0],
              [[ps.rank_from_shape(output_shapes[0]) + self.axis]],
              [total_size],
          )
          return tf.identity(
              tf.convert_to_tensor(
                  inverse_event_shape,
                  dtype_hint=tf.int32,
                  name="inverse_event_shape",
              )
          )
  
imports: import numbers
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.internal import tensorshape_util
label_desc: Use image node_id 11 for calling the Split obj's underlying member method code with example usage: obj._inverse_event_shape_tensor(output_shapes) and returns: tf