parameters: (start, limit=None, delta=1, dtype=None, name="range")
docstring: Creates a sequence of numbers.
  
    Creates a sequence of numbers that begins at `start` and extends by
    increments of `delta` up to but not including `limit`.
  
    The dtype of the resulting tensor is inferred from the inputs unless
    it is provided explicitly.
  
    Like the Python builtin `range`, `start` defaults to 0, so that
    `range(n) = range(0, n)`.
  
    For example:
  
    ```python
    # 'start' is 3
    # 'limit' is 18
    # 'delta' is 3
    tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15]
  
    # 'start' is 3
    # 'limit' is 1
    # 'delta' is -0.5
    tf.range(start, limit, delta) ==> [3, 2.5, 2, 1.5]
  
    # 'limit' is 5
    tf.range(limit) ==> [0, 1, 2, 3, 4]
    ```
  
    Args:
      start: A 0-D `Tensor` (scalar). Acts as first entry in the range if
        `limit` is not None; otherwise, acts as range limit and first entry
        defaults to 0.
      limit: A 0-D `Tensor` (scalar). Upper limit of sequence,
        exclusive. If None, defaults to the value of `start` while the first
        entry of the range defaults to 0.
      delta: A 0-D `Tensor` (scalar). Number that increments
        `start`. Defaults to 1.
      dtype: The type of the elements of the resulting tensor.
      name: A name for the operation. Defaults to "range".
  
    Returns:
      An 1-D `Tensor` of type `dtype`.
docstring_summary: Creates a sequence of numbers.
docstring_tokens: Creates a sequence of numbers .
function: def range(start, limit=None, delta=1, dtype=None, name="range"):
    """Creates a sequence of numbers.
  
    Creates a sequence of numbers that begins at `start` and extends by
    increments of `delta` up to but not including `limit`.
  
    The dtype of the resulting tensor is inferred from the inputs unless
    it is provided explicitly.
  
    Like the Python builtin `range`, `start` defaults to 0, so that
    `range(n) = range(0, n)`.
  
    For example:
  
    ```python
    # 'start' is 3
    # 'limit' is 18
    # 'delta' is 3
    tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15]
  
    # 'start' is 3
    # 'limit' is 1
    # 'delta' is -0.5
    tf.range(start, limit, delta) ==> [3, 2.5, 2, 1.5]
  
    # 'limit' is 5
    tf.range(limit) ==> [0, 1, 2, 3, 4]
    ```
  
    Args:
      start: A 0-D `Tensor` (scalar). Acts as first entry in the range if
        `limit` is not None; otherwise, acts as range limit and first entry
        defaults to 0.
      limit: A 0-D `Tensor` (scalar). Upper limit of sequence,
        exclusive. If None, defaults to the value of `start` while the first
        entry of the range defaults to 0.
      delta: A 0-D `Tensor` (scalar). Number that increments
        `start`. Defaults to 1.
      dtype: The type of the elements of the resulting tensor.
      name: A name for the operation. Defaults to "range".
  
    Returns:
      An 1-D `Tensor` of type `dtype`.
    """
    if limit is None:
      start, limit = 0, start
  
    with ops.name_scope(name, "Range", [start, limit, delta]) as name:
      start = ops.convert_to_tensor(start, dtype=dtype, name="start")
      limit = ops.convert_to_tensor(limit, dtype=dtype, name="limit")
      delta = ops.convert_to_tensor(delta, dtype=dtype, name="delta")
  
      # infer dtype if not explicitly provided
      if dtype is None:
        dtype_hierarchy = [dtypes.int32, dtypes.int64, dtypes.float32,
                           dtypes.float64]
        assert all(arg.dtype in dtype_hierarchy for arg in [start, limit, delta])
        inferred_dtype = max([arg.dtype for arg in [start, limit, delta]],
                             key=dtype_hierarchy.index)
  
        start = cast(start, inferred_dtype)
        limit = cast(limit, inferred_dtype)
        delta = cast(delta, inferred_dtype)
  
      return gen_math_ops._range(start, limit, delta, name=name)
function_tokens: def range ( start , limit = None , delta = 1 , dtype = None , name = "range" ) : if limit is None : start , limit = 0 , start with ops . name_scope ( name , "Range" , [ start , limit , delta ] ) as name : start = ops . convert_to_tensor ( start , dtype = dtype , name = "start" ) limit = ops . convert_to_tensor ( limit , dtype = dtype , name = "limit" ) delta = ops . convert_to_tensor ( delta , dtype = dtype , name = "delta" ) # infer dtype if not explicitly provided if dtype is None : dtype_hierarchy = [ dtypes . int32 , dtypes . int64 , dtypes . float32 , dtypes . float64 ] assert all ( arg . dtype in dtype_hierarchy for arg in [ start , limit , delta ] ) inferred_dtype = max ( [ arg . dtype for arg in [ start , limit , delta ] ] , key = dtype_hierarchy . index ) start = cast ( start , inferred_dtype ) limit = cast ( limit , inferred_dtype ) delta = cast ( delta , inferred_dtype ) return gen_math_ops . _range ( start , limit , delta , name = name )