text: ### `tf.sparse_to_indicator(sp_input, vocab_size, name=None)` {#sparse_to_indicator}
  
  Converts a `SparseTensor` of ids into a dense bool indicator tensor.
  
  The last dimension of `sp_input.indices` is discarded and replaced with
  the values of `sp_input`.  If `sp_input.shape = [D0, D1, ..., Dn, K]`, then
  `output.shape = [D0, D1, ..., Dn, vocab_size]`, where
  
      output[d_0, d_1, ..., d_n, sp_input[d_0, d_1, ..., d_n, k]] = True
  
  and False elsewhere in `output`.
  
  For example, if `sp_input.shape = [2, 3, 4]` with non-empty values:
  
      [0, 0, 0]: 0
      [0, 1, 0]: 10
      [1, 0, 3]: 103
      [1, 1, 2]: 150
      [1, 1, 3]: 149
      [1, 1, 4]: 150
      [1, 2, 1]: 121
  
  and `vocab_size = 200`, then the output will be a `[2, 3, 200]` dense bool
  tensor with False everywhere except at positions
  
      (0, 0, 0), (0, 1, 10), (1, 0, 103), (1, 1, 149), (1, 1, 150),
      (1, 2, 121).
  
  Note that repeats are allowed in the input SparseTensor.
  This op is useful for converting `SparseTensor`s into dense formats for
  compatibility with ops that expect dense tensors.
  
  The input `SparseTensor` must be in row-major order.
  
  ##### Args:
  
  
  *  <b>`sp_input`</b>: A `SparseTensor` with `values` property of type `int32` or
      `int64`.
  *  <b>`vocab_size`</b>: A scalar int64 Tensor (or Python int) containing the new size
      of the last dimension, `all(0 <= sp_input.values < vocab_size)`.
  *  <b>`name`</b>: A name prefix for the returned tensors (optional)
  
  ##### Returns:
  
    A dense bool indicator tensor representing the indices with specified value.
  
  ##### Raises:
  
  
  *  <b>`TypeError`</b>: If `sp_input` is not a `SparseTensor`.
  
  