parameters: (start, stop=None, step=1, dtype='int32')
return_statement: return result
docstring: Creates a 1D tensor containing a sequence of integers.
  
    The function arguments use the same convention as
    Theano's arange: if only one argument is provided,
    it is in fact the "stop" argument.
  
    The default type of the returned tensor is `'int32'` to
    match TensorFlow's default.
  
    Arguments:
        start: Start value.
        stop: Stop value.
        step: Difference between two successive values.
        dtype: Integer dtype to use.
  
    Returns:
        An integer tensor.
docstring_summary: Creates a 1D tensor containing a sequence of integers.
docstring_tokens: Creates a 1D tensor containing a sequence of integers .
function: def arange(start, stop=None, step=1, dtype='int32'):
    """Creates a 1D tensor containing a sequence of integers.
  
    The function arguments use the same convention as
    Theano's arange: if only one argument is provided,
    it is in fact the "stop" argument.
  
    The default type of the returned tensor is `'int32'` to
    match TensorFlow's default.
  
    Arguments:
        start: Start value.
        stop: Stop value.
        step: Difference between two successive values.
        dtype: Integer dtype to use.
  
    Returns:
        An integer tensor.
  
    """
    # Match the behavior of numpy and Theano by returning an empty seqence.
    if stop is None and start < 0:
      start = 0
    result = math_ops.range(start, limit=stop, delta=step, name='arange')
    if dtype != 'int32':
      result = cast(result, dtype)
    return result
function_tokens: def arange ( start , stop = None , step = 1 , dtype = 'int32' ) : # Match the behavior of numpy and Theano by returning an empty seqence. if stop is None and start < 0 : start = 0 result = math_ops . range ( start , limit = stop , delta = step , name = 'arange' ) if dtype != 'int32' : result = cast ( result , dtype ) return result