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-### `tf.scan(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, swap_memory=False, name=None)` {#scan}
-
-scan on the list of tensors unpacked from `elems` on dimension 0.
-
-This scan operator repeatedly applies the callable `fn` to a sequence
-of elements from first to last. The elements are made of the tensors
-unpacked from `elems` on dimension 0. The callable fn takes two tensors as
-arguments. The first argument is the accumulated value computed from the
-preceding invocation of fn. If `initializer` is None, `elems` must contain
-at least one element, and its first element is used as the initializer.
-
-Suppose that `elems` is unpacked into `values`, a list of tensors. The shape
-of the result tensor is `[len(values)] + fn(initializer, values[0]).shape`.
-
-##### Args:
-
-
-*  <b>`fn`</b>: The callable to be performed.
-*  <b>`elems`</b>: A tensor to be unpacked on dimension 0.
-*  <b>`initializer`</b>: (optional) The initial value for the accumulator.
-*  <b>`parallel_iterations`</b>: (optional) The number of iterations allowed to run
-                       in parallel.
-*  <b>`back_prop`</b>: (optional) True enables back propagation.
-*  <b>`swap_memory`</b>: (optional) True enables GPU-CPU memory swapping.
-*  <b>`name`</b>: (optional) Name prefix for the returned tensors.
-
-##### Returns:
-
-  A tensor that packs the results of applying `fn` to the list of tensors
-  unpacked from `elems`, from first to last.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if `fn` is not callable.
-
-##### Example:
-
-  ```python
-  elems = [1, 2, 3, 4, 5, 6]
-  sum = scan(lambda a, x: a + x, elems)
-  # sum == [1, 3, 6, 10, 15, 21]
-  ```
-