parameters: (input, k=1, sorted=True, name=None)
return_statement: return gen_nn_ops.top_kv2(input, k=k, sorted=sorted, name=name)
docstring: Finds values and indices of the `k` largest entries for the last dimension.
  
    If the input is a vector (rank=1), finds the `k` largest entries in the vector
    and outputs their values and indices as vectors.  Thus `values[j]` is the
    `j`-th largest entry in `input`, and its index is `indices[j]`.
  
    >>> result = tf.math.top_k([1, 2, 98, 1, 1, 99, 3, 1, 3, 96, 4, 1],
    ...                         k=3)
    >>> result.values.numpy()
    array([99, 98, 96], dtype=int32)
    >>> result.indices.numpy()
    array([5, 2, 9], dtype=int32)
  
    For matrices (resp. higher rank input), computes the top `k` entries in each
    row (resp. vector along the last dimension).  Thus,
  
    >>> input = tf.random.normal(shape=(3,4,5,6))
    >>> k = 2
    >>> values, indices  = tf.math.top_k(input, k=k)
    >>> values.shape.as_list()
    [3, 4, 5, 2]
    >>>
    >>> values.shape == indices.shape == input.shape[:-1] + [k]
    True
  
    The indices can be used to `gather` from a tensor who's shape matches `input`.
  
    >>> gathered_values = tf.gather(input, indices, batch_dims=-1)
    >>> assert tf.reduce_all(gathered_values == values)
  
    If two elements are equal, the lower-index element appears first.
  
    >>> result = tf.math.top_k([1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0],
    ...                        k=3)
    >>> result.indices.numpy()
    array([0, 1, 3], dtype=int32)
  
    Args:
      input: 1-D or higher `Tensor` with last dimension at least `k`.
      k: 0-D `int32` `Tensor`.  Number of top elements to look for along the last
        dimension (along each row for matrices).
      sorted: If true the resulting `k` elements will be sorted by the values in
        descending order.
      name: Optional name for the operation.
  
    Returns:
      A tuple with two named fields:
      values: The `k` largest elements along each last dimensional slice.
      indices: The indices of `values` within the last dimension of `input`.
docstring_summary: Finds values and indices of the `k` largest entries for the last dimension.
docstring_tokens: Finds values and indices of the k largest entries for the last dimension .
function: def top_k(input, k=1, sorted=True, name=None):  # pylint: disable=redefined-builtin
    """Finds values and indices of the `k` largest entries for the last dimension.
  
    If the input is a vector (rank=1), finds the `k` largest entries in the vector
    and outputs their values and indices as vectors.  Thus `values[j]` is the
    `j`-th largest entry in `input`, and its index is `indices[j]`.
  
    >>> result = tf.math.top_k([1, 2, 98, 1, 1, 99, 3, 1, 3, 96, 4, 1],
    ...                         k=3)
    >>> result.values.numpy()
    array([99, 98, 96], dtype=int32)
    >>> result.indices.numpy()
    array([5, 2, 9], dtype=int32)
  
    For matrices (resp. higher rank input), computes the top `k` entries in each
    row (resp. vector along the last dimension).  Thus,
  
    >>> input = tf.random.normal(shape=(3,4,5,6))
    >>> k = 2
    >>> values, indices  = tf.math.top_k(input, k=k)
    >>> values.shape.as_list()
    [3, 4, 5, 2]
    >>>
    >>> values.shape == indices.shape == input.shape[:-1] + [k]
    True
  
    The indices can be used to `gather` from a tensor who's shape matches `input`.
  
    >>> gathered_values = tf.gather(input, indices, batch_dims=-1)
    >>> assert tf.reduce_all(gathered_values == values)
  
    If two elements are equal, the lower-index element appears first.
  
    >>> result = tf.math.top_k([1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0],
    ...                        k=3)
    >>> result.indices.numpy()
    array([0, 1, 3], dtype=int32)
  
    Args:
      input: 1-D or higher `Tensor` with last dimension at least `k`.
      k: 0-D `int32` `Tensor`.  Number of top elements to look for along the last
        dimension (along each row for matrices).
      sorted: If true the resulting `k` elements will be sorted by the values in
        descending order.
      name: Optional name for the operation.
  
    Returns:
      A tuple with two named fields:
      values: The `k` largest elements along each last dimensional slice.
      indices: The indices of `values` within the last dimension of `input`.
    """
    return gen_nn_ops.top_kv2(input, k=k, sorted=sorted, name=name)
function_tokens: def top_k ( input , k = 1 , sorted = True , name = None ) : # pylint: disable=redefined-builtin return gen_nn_ops . top_kv2 ( input , k = k , sorted = sorted , name = name )