parameters: (x, out_idx=dtypes.int32, name=None)
return_statement: return gen_array_ops.unique_with_counts(x, out_idx, name)
docstring: Finds unique elements in a 1-D tensor.
  
    See also `tf.unique`.
  
    This operation returns a tensor `y` containing all of the unique elements
    of `x` sorted in the same order that they occur in `x`. This operation
    also returns a tensor `idx` the same size as `x` that contains the index
    of each value of `x` in the unique output `y`. Finally, it returns a
    third tensor `count` that contains the count of each element of `y`
    in `x`. In other words:
  
      y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]
  
    Example usage:
  
    >>> x = tf.constant([1, 1, 2, 4, 4, 4, 7, 8, 8])
    >>> y, idx, count = unique_with_counts(x)
    >>> y
    <tf.Tensor: id=8, shape=(5,), dtype=int32,
    numpy=array([1, 2, 4, 7, 8], dtype=int32)>
    >>> idx
    <tf.Tensor: id=9, shape=(9,), dtype=int32,
    numpy=array([0, 0, 1, 2, 2, 2, 3, 4, 4], dtype=int32)>
    >>> count
    <tf.Tensor: id=10, shape=(5,), dtype=int32,
    numpy=array([2, 1, 3, 1, 2], dtype=int32)>
  
    Args:
      x: A Tensor. 1-D.
      out_idx: An optional tf.DType from: tf.int32, tf.int64. Defaults to
        tf.int32.
      name: A name for the operation (optional).
  
    Returns:
      A tuple of Tensor objects (y, idx, count).
        y: A Tensor. Has the same type as x.
        idx: A Tensor of type out_idx.
        count: A Tensor of type out_idx.
docstring_summary: Finds unique elements in a 1-D tensor.
docstring_tokens: Finds unique elements in a 1 - D tensor .
function: def unique_with_counts(x, out_idx=dtypes.int32, name=None):
    """Finds unique elements in a 1-D tensor.
  
    See also `tf.unique`.
  
    This operation returns a tensor `y` containing all of the unique elements
    of `x` sorted in the same order that they occur in `x`. This operation
    also returns a tensor `idx` the same size as `x` that contains the index
    of each value of `x` in the unique output `y`. Finally, it returns a
    third tensor `count` that contains the count of each element of `y`
    in `x`. In other words:
  
      y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]
  
    Example usage:
  
    >>> x = tf.constant([1, 1, 2, 4, 4, 4, 7, 8, 8])
    >>> y, idx, count = unique_with_counts(x)
    >>> y
    <tf.Tensor: id=8, shape=(5,), dtype=int32,
    numpy=array([1, 2, 4, 7, 8], dtype=int32)>
    >>> idx
    <tf.Tensor: id=9, shape=(9,), dtype=int32,
    numpy=array([0, 0, 1, 2, 2, 2, 3, 4, 4], dtype=int32)>
    >>> count
    <tf.Tensor: id=10, shape=(5,), dtype=int32,
    numpy=array([2, 1, 3, 1, 2], dtype=int32)>
  
    Args:
      x: A Tensor. 1-D.
      out_idx: An optional tf.DType from: tf.int32, tf.int64. Defaults to
        tf.int32.
      name: A name for the operation (optional).
  
    Returns:
      A tuple of Tensor objects (y, idx, count).
        y: A Tensor. Has the same type as x.
        idx: A Tensor of type out_idx.
        count: A Tensor of type out_idx.
  
    """
    # TODO(yongtang): switch to v2 once API deprecation
    # period (3 weeks) pass.
    # TODO(yongtang): The documentation should also
    # be updated when switch  to v2.
    return gen_array_ops.unique_with_counts(x, out_idx, name)
function_tokens: def unique_with_counts ( x , out_idx = dtypes . int32 , name = None ) : # TODO(yongtang): switch to v2 once API deprecation # period (3 weeks) pass. # TODO(yongtang): The documentation should also # be updated when switch  to v2. return gen_array_ops . unique_with_counts ( x , out_idx , name )