(start, stop=None, step=1, dtype=None)
return arrays_lib.tensor_to_ndarray(
      tf.cast(tf.range(start, limit=stop, delta=step), dtype=dtype))
Returns `step`-separated values in the range [start, stop).

  Args:
    start: Start of the interval. Included in the range.
    stop: End of the interval. If not specified, `start` is treated as 0 and
      `start` value is used as `stop`. If specified, it is not included in the
      range if `step` is integer. When `step` is floating point, it may or may
      not be included.
    step: The difference between 2 consecutive values in the output range. It is
      recommended to use `linspace` instead of using non-integer values for
      `step`.
    dtype: Optional. Type of the resulting ndarray. Could be a python type, a
      NumPy type or a TensorFlow `DType`. If not provided, the largest type of
      `start`, `stop`, `step` is used.

  Raises:
    ValueError: If step is zero.
Returns `step`-separated values in the range [start, stop).
Returns step - separated values in the range [ start stop ) .
def arange(start, stop=None, step=1, dtype=None):
  """Returns `step`-separated values in the range [start, stop).

  Args:
    start: Start of the interval. Included in the range.
    stop: End of the interval. If not specified, `start` is treated as 0 and
      `start` value is used as `stop`. If specified, it is not included in the
      range if `step` is integer. When `step` is floating point, it may or may
      not be included.
    step: The difference between 2 consecutive values in the output range. It is
      recommended to use `linspace` instead of using non-integer values for
      `step`.
    dtype: Optional. Type of the resulting ndarray. Could be a python type, a
      NumPy type or a TensorFlow `DType`. If not provided, the largest type of
      `start`, `stop`, `step` is used.

  Raises:
    ValueError: If step is zero.
  """
  if not step:
    raise ValueError('step must be non-zero.')
  if dtype:
    dtype = utils.result_type(dtype)
  else:
    if stop is None:
      dtype = utils.result_type(start, step)
    else:
      dtype = utils.result_type(start, step, stop)
  if step > 0 and ((stop is not None and start > stop) or
                   (stop is None and start < 0)):
    return array([], dtype=dtype)
  if step < 0 and ((stop is not None and start < stop) or
                   (stop is None and start > 0)):
    return array([], dtype=dtype)
  # TODO(srbs): There are some bugs when start or stop is float type and dtype
  # is integer type.
  return arrays_lib.tensor_to_ndarray(
      tf.cast(tf.range(start, limit=stop, delta=step), dtype=dtype))
def arange ( start , stop = None , step = 1 , dtype = None ) : if not step : raise ValueError ( 'step must be non-zero.' ) if dtype : dtype = utils . result_type ( dtype ) else : if stop is None : dtype = utils . result_type ( start , step ) else : dtype = utils . result_type ( start , step , stop ) if step > 0 and ( ( stop is not None and start > stop ) or ( stop is None and start < 0 ) ) : return array ( [ ] , dtype = dtype ) if step < 0 and ( ( stop is not None and start < stop ) or ( stop is None and start > 0 ) ) : return array ( [ ] , dtype = dtype ) # TODO(srbs): There are some bugs when start or stop is float type and dtype # is integer type. return arrays_lib . tensor_to_ndarray ( tf . cast ( tf . range ( start , limit = stop , delta = step ) , dtype = dtype ) )