parameters: (sequences, name=None)
docstring: Computes componentwise maxes along each sequence in a `Jagged` tensor.
  
    Equivalent to `pack([reduce_max(s, axis=0) for s in sequences])`.
  
    Args:
      sequences: `Jagged` tensor of sequences.
      name: Optional name for this operation.
  
    Returns:
      Tensor of componentwise maxima.
docstring_summary: Computes componentwise maxes along each sequence in a `Jagged` tensor.
docstring_tokens: Computes componentwise maxes along each sequence in a Jagged tensor .
function: def reduce_max(sequences, name=None):
    """Computes componentwise maxes along each sequence in a `Jagged` tensor.
  
    Equivalent to `pack([reduce_max(s, axis=0) for s in sequences])`.
  
    Args:
      sequences: `Jagged` tensor of sequences.
      name: Optional name for this operation.
  
    Returns:
      Tensor of componentwise maxima.
    """
    if not isinstance(sequences, Jagged):
      raise TypeError('Expected Jagged sequences, got %s' % type(Jagged))
    with tf.name_scope(name, 'jagged_reduce_max') as name:
      sizes, flat = unjagged(sequences)
      sizes_sizes, sizes_flat = unjagged(sizes)
      maxes, _ = gen_jagged_ops.jagged_max(sizes_flat, flat, name=name)
      return jagged(sizes_sizes, maxes)
function_tokens: def reduce_max ( sequences , name = None ) : if not isinstance ( sequences , Jagged ) : raise TypeError ( 'Expected Jagged sequences, got %s' % type ( Jagged ) ) with tf . name_scope ( name , 'jagged_reduce_max' ) as name : sizes , flat = unjagged ( sequences ) sizes_sizes , sizes_flat = unjagged ( sizes ) maxes , _ = gen_jagged_ops . jagged_max ( sizes_flat , flat , name = name ) return jagged ( sizes_sizes , maxes )