|---| | [ View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/ops/numpy_ops/np_array_ops.py#L259-L301) | TensorFlow variant of NumPy's `arange`. tf.experimental.numpy.arange( start, stop=None, step=1, dtype=None ) ### Used in the notebooks | Used in the guide | |---| | - [NumPy API on TensorFlow](https://www.tensorflow.org/guide/tf_numpy) | Unsupported arguments: `start_or_stop`, `device`, `like`. 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. See the NumPy documentation for [`numpy.arange`](https://numpy.org/doc/stable/reference/generated/numpy.arange.html).