def from_value_rowids(cls,
                        value_rowids,
                        nrows=None,
                        validate=True,
                        preferred_dtype=None,
                        dtype=None,
                        dtype_hint=None):
    """Creates a `RowPartition` with rows partitioned by `value_rowids`.

    This `RowPartition` divides a sequence `values` into rows by specifying
    which row each value should be added to:

    ```python
    partitioned_rows = [[] for _ in nrows]
    for (value, rowid) in zip(values, value_rowids):
      partitioned_rows[rowid].append(value)
    ``

    Args:
      value_rowids: A 1-D integer tensor with shape `[nvals]`, which corresponds
        one-to-one with `values`, and specifies each value's row index.  Must be
        nonnegative, and must be sorted in ascending order.
      nrows: An integer scalar specifying the number of rows.  This should be
        specified if the `RowPartition` may containing empty training rows. Must
        be greater than `value_rowids[-1]` (or greater than or equal to zero if
        `value_rowids` is empty). Defaults to `value_rowids[-1] + 1` (or zero if
        `value_rowids` is empty).
      validate: If true, then use assertions to check that the arguments form a
        valid `RowPartition`.
      preferred_dtype: Deprecated synonym of dtype_hint.
      dtype: Optional dtype for the RowPartition. If missing, the type
        is inferred from the type of `value_rowids`, dtype_hint, or tf.int64.
      dtype_hint: Optional dtype for the RowPartition, used when dtype
        is None. In some cases, a caller may not have a dtype in mind when
        converting to a tensor, so dtype_hint can be used as a soft preference.
        If the conversion to `dtype_hint` is not possible, this argument has no
        effect.

    Returns:
      A `RowPartition`.

    Raises:
      ValueError: If `nrows` is incompatible with `value_rowids`.

    #### Example:

    >>> print(RowPartition.from_value_rowids(
    ...     value_rowids=[0, 0, 0, 0, 2, 2, 2, 3],
    ...     nrows=4))
    tf.RowPartition(row_splits=[0 4 4 7 8])
    """
    dtype_hint = _get_dtype_hint(preferred_dtype, dtype_hint)
    # Local import bincount_ops to avoid import-cycle since bincount_ops
    # imports ragged_tensor.
    from tensorflow.python.ops import bincount_ops  # pylint: disable=g-import-not-at-top
    if not isinstance(validate, bool):
      raise TypeError("validate must have type bool")
    with ops.name_scope(None, "RowPartitionFromValueRowIds",
                        [value_rowids, nrows]):
      value_rowids = cls._convert_row_partition(
          value_rowids, "value_rowids", dtype_hint=dtype_hint, dtype=dtype)
      if nrows is None:
        const_rowids = tensor_util.constant_value(value_rowids)
        if const_rowids is None:
          nrows = array_ops.concat([value_rowids[-1:], [-1]], axis=0)[0] + 1
          const_nrows = None
        else:
          const_nrows = const_rowids[-1] + 1 if const_rowids.size > 0 else 0
          nrows = ops.convert_to_tensor(
              const_nrows, value_rowids.dtype, name="nrows")
      else:
        nrows = ops.convert_to_tensor(nrows, value_rowids.dtype, "nrows")
        const_nrows = tensor_util.constant_value(nrows)
        if const_nrows is not None:
          if const_nrows < 0:
            raise ValueError("Expected nrows >= 0; got %d" % const_nrows)
          const_rowids = tensor_util.constant_value(value_rowids)
          if const_rowids is not None and const_rowids.size > 0:
            if not const_nrows >= const_rowids[-1] + 1:
              raise ValueError(
                  "Expected nrows >= value_rowids[-1] + 1; got nrows=%d, "
                  "value_rowids[-1]=%d" % (const_nrows, const_rowids[-1]))

      value_rowids.shape.assert_has_rank(1)
      nrows.shape.assert_has_rank(0)

      if validate:
        msg = ("Arguments to from_value_rowids do not form a valid "
               "RowPartition")
        checks = [
            check_ops.assert_rank(value_rowids, 1, message=msg),
            check_ops.assert_rank(nrows, 0, message=msg),
            check_ops.assert_non_negative(value_rowids[:1], message=msg),
            _assert_monotonic_increasing(value_rowids, message=msg),
            check_ops.assert_less(value_rowids[-1:], nrows, message=msg),
        ]
        value_rowids = control_flow_ops.with_dependencies(checks, value_rowids)

      # Convert value_rowids & nrows to row_splits.
      # Note: we don't use segment_ids_to_row_splits() here because we want
      # to save the intermediate value `row_lengths`, so we can cache it.
      # TODO(b/116708836) Upgrade bincount to accept int64 so we can skip the
      # cast.
      value_rowids_int32 = math_ops.cast(value_rowids, dtypes.int32)
      nrows_int32 = math_ops.cast(nrows, dtypes.int32)
      row_lengths = bincount_ops.bincount(
          value_rowids_int32,
          minlength=nrows_int32,
          maxlength=nrows_int32,
          dtype=value_rowids.dtype)
      row_splits = array_ops.concat([[0], math_ops.cumsum(row_lengths)], axis=0)
      if const_nrows is not None:
        row_lengths.set_shape([const_nrows])
        row_splits.set_shape([const_nrows + 1])

      return cls(
          row_splits=row_splits,
          row_lengths=row_lengths,
          value_rowids=value_rowids,
          nrows=nrows,
          internal=_row_partition_factory_key)