code: def scan_v2(
      fn,
      elems,
      initializer=None,
      parallel_iterations=10,
      back_prop=True,
      swap_memory=False,
      infer_shape=True,
      reverse=False,
      name=None,
  ):
      """scan on the list of tensors unpacked from `elems` on dimension 0.
  
      The simplest version of `scan` 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, and the second is the value at the current
      position of `elems`. 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`.
      If reverse=True, it's fn(initializer, values[-1]).shape.
  
      This method also allows multi-arity `elems` and accumulator.  If `elems`
      is a (possibly nested) list or tuple of tensors, then each of these tensors
      must have a matching first (unpack) dimension.  The second argument of
      `fn` must match the structure of `elems`.
  
      If no `initializer` is provided, the output structure and dtypes of `fn`
      are assumed to be the same as its input; and in this case, the first
      argument of `fn` must match the structure of `elems`.
  
      If an `initializer` is provided, then the output of `fn` must have the same
      structure as `initializer`; and the first argument of `fn` must match
      this structure.
  
      For example, if `elems` is `(t1, [t2, t3])` and `initializer` is
      `[i1, i2]` then an appropriate signature for `fn` in `python2` is:
      `fn = lambda (acc_p1, acc_p2), (t1, [t2, t3]):` and `fn` must return a list,
      `[acc_n1, acc_n2]`.  An alternative correct signature for `fn`, and the
       one that works in `python3`, is:
      `fn = lambda a, t:`, where `a` and `t` correspond to the input tuples.
  
      Args:
        fn: The callable to be performed.  It accepts two arguments.  The first will
          have the same structure as `initializer` if one is provided, otherwise it
          will have the same structure as `elems`.  The second will have the same
          (possibly nested) structure as `elems`.  Its output must have the same
          structure as `initializer` if one is provided, otherwise it must have the
          same structure as `elems`.
        elems: A tensor or (possibly nested) sequence of tensors, each of which will
          be unpacked along their first dimension.  The nested sequence of the
          resulting slices will be the first argument to `fn`.
        initializer: (optional) A tensor or (possibly nested) sequence of tensors,
          initial value for the accumulator, and the expected output type of `fn`.
        parallel_iterations: (optional) The number of iterations allowed to run in
          parallel.
        back_prop: (optional) Deprecated. False disables support for back
          propagation. Prefer using `tf.stop_gradient` instead.
        swap_memory: (optional) True enables GPU-CPU memory swapping.
        infer_shape: (optional) False disables tests for consistent output shapes.
        reverse: (optional) True scans the tensor last to first (instead of first to
          last).
        name: (optional) Name prefix for the returned tensors.
  
      Returns:
        A tensor or (possibly nested) sequence of tensors.  Each tensor packs the
        results of applying `fn` to tensors unpacked from `elems` along the first
        dimension, and the previous accumulator value(s), from first to last (or
        last to first, if `reverse=True`).
  
      Raises:
        TypeError: if `fn` is not callable or the structure of the output of
          `fn` and `initializer` do not match.
        ValueError: if the lengths of the output of `fn` and `initializer`
          do not match.
  
      Examples:
        ```python
        elems = np.array([1, 2, 3, 4, 5, 6])
        sum = scan(lambda a, x: a + x, elems)
        # sum == [1, 3, 6, 10, 15, 21]
        sum = scan(lambda a, x: a + x, elems, reverse=True)
        # sum == [21, 20, 18, 15, 11, 6]
        ```
  
        ```python
        elems = np.array([1, 2, 3, 4, 5, 6])
        initializer = np.array(0)
        sum_one = scan(
            lambda a, x: x[0] - x[1] + a, (elems + 1, elems), initializer)
        # sum_one == [1, 2, 3, 4, 5, 6]
        ```
  
        ```python
        elems = np.array([1, 0, 0, 0, 0, 0])
        initializer = (np.array(0), np.array(1))
        fibonaccis = scan(lambda a, _: (a[1], a[0] + a[1]), elems, initializer)
        # fibonaccis == ([1, 1, 2, 3, 5, 8], [1, 2, 3, 5, 8, 13])
        ```
      """
      return scan(
          fn=fn,
          elems=elems,
          initializer=initializer,
          parallel_iterations=parallel_iterations,
          back_prop=back_prop,
          swap_memory=swap_memory,
          infer_shape=infer_shape,
          reverse=reverse,
          name=name,
      )
  
imports: from tensorflow.core.framework import attr_value_pb2
  from tensorflow.python.eager import context
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import function
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_functional_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import tensor_array_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.ops import while_loop
  from tensorflow.python.ops.gen_functional_ops import remote_call
  from tensorflow.python.ops.gen_functional_ops import symbolic_gradient
  from tensorflow.python.util import deprecation
  from tensorflow.python.util import dispatch
  from tensorflow.python.util import nest
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 6 for calling a global function with example usage: scan_v2(fn, elems, initializer, parallel_iterations, back_prop, swap_memory, infer_shape, reverse, name) and returns: scan