code: def while_loop(
      cond,
      body,
      loop_vars,
      shape_invariants=None,
      parallel_iterations=10,
      back_prop=True,
      swap_memory=False,
      name=None,
      maximum_iterations=None,
      return_same_structure=False,
  ):
      """Repeat `body` while the condition `cond` is true.
  
      `cond` is a callable returning a boolean scalar tensor. `body` is a callable
      returning a (possibly nested) tuple, namedtuple or list of tensors of the same
      arity (length and structure) and types as `loop_vars`. `loop_vars` is a
      (possibly nested) tuple, namedtuple or list of tensors that is passed to both
      `cond` and `body`. `cond` and `body` both take as many arguments as there are
      `loop_vars`.
  
      In addition to regular Tensors or IndexedSlices, the body may accept and
      return TensorArray objects.  The flows of the TensorArray objects will
      be appropriately forwarded between loops and during gradient calculations.
  
      Note that `while_loop` calls `cond` and `body` *exactly once* (inside the
      call to `while_loop`, and not at all during `Session.run()`). `while_loop`
      stitches together the graph fragments created during the `cond` and `body`
      calls with some additional graph nodes to create the graph flow that
      repeats `body` until `cond` returns false.
  
      For correctness, `tf.while_loop()` strictly enforces shape invariants for
      the loop variables. A shape invariant is a (possibly partial) shape that
      is unchanged across the iterations of the loop. An error will be raised
      if the shape of a loop variable after an iteration is determined to be more
      general than or incompatible with its shape invariant. For example, a shape
      of [11, None] is more general than a shape of [11, 17], and [11, 21] is not
      compatible with [11, 17]. By default (if the argument `shape_invariants` is
      not specified), it is assumed that the initial shape of each tensor in
      `loop_vars` is the same in every iteration. The `shape_invariants` argument
      allows the caller to specify a less specific shape invariant for each loop
      variable, which is needed if the shape varies between iterations. The
      `tf.Tensor.set_shape`
      function may also be used in the `body` function to indicate that
      the output loop variable has a particular shape. The shape invariant for
      SparseTensor and IndexedSlices are treated specially as follows:
  
      a) If a loop variable is a SparseTensor, the shape invariant must be
      TensorShape([r]) where r is the rank of the dense tensor represented
      by the sparse tensor. It means the shapes of the three tensors of the
      SparseTensor are ([None], [None, r], [r]). NOTE: The shape invariant here
      is the shape of the SparseTensor.dense_shape property. It must be the shape of
      a vector.
  
      b) If a loop variable is an IndexedSlices, the shape invariant must be
      a shape invariant of the values tensor of the IndexedSlices. It means
      the shapes of the three tensors of the IndexedSlices are (shape, [shape[0]],
      [shape.ndims]).
  
      `while_loop` implements non-strict semantics, enabling multiple iterations
      to run in parallel. The maximum number of parallel iterations can be
      controlled by `parallel_iterations`, which gives users some control over
      memory consumption and execution order. For correct programs, `while_loop`
      should return the same result for any parallel_iterations > 0.
  
      For training, TensorFlow stores the tensors that are produced in the
      forward inference and are needed in back propagation. These tensors are a
      main source of memory consumption and often cause OOM errors when training
      on GPUs. When the flag swap_memory is true, we swap out these tensors from
      GPU to CPU. This for example allows us to train RNN models with very long
      sequences and large batches.
  
      Args:
        cond: A callable that represents the termination condition of the loop.
        body: A callable that represents the loop body.
        loop_vars: A (possibly nested) tuple, namedtuple or list of numpy array,
          `Tensor`, and `TensorArray` objects.
        shape_invariants: The shape invariants for the loop variables.
        parallel_iterations: The number of iterations allowed to run in parallel. It
          must be a positive integer.
        back_prop: Whether backprop is enabled for this while loop.
        swap_memory: Whether GPU-CPU memory swap is enabled for this loop.
        name: Optional name prefix for the returned tensors.
        maximum_iterations: Optional maximum number of iterations of the while loop
          to run.  If provided, the `cond` output is AND-ed with an additional
          condition ensuring the number of iterations executed is no greater than
          `maximum_iterations`.
        return_same_structure: If True, output has same structure as `loop_vars`. If
          eager execution is enabled, this is ignored (and always treated as True).
  
      Returns:
        The output tensors for the loop variables after the loop.
         If `return_same_structure` is True, the return value has the same
         structure as `loop_vars`.
         If `return_same_structure` is False, the return value is a Tensor,
         TensorArray or IndexedSlice if the length of `loop_vars` is 1, or a list
         otherwise.
  
      Raises:
        TypeError: if `cond` or `body` is not callable.
        ValueError: if `loop_vars` is empty.
  
      Example:
  
      ```python
      i = tf.constant(0)
      c = lambda i: tf.less(i, 10)
      b = lambda i: tf.add(i, 1)
      r = tf.while_loop(c, b, [i])
      ```
  
      Example with nesting and a namedtuple:
  
      ```python
      import collections
      Pair = collections.namedtuple('Pair', 'j, k')
      ijk_0 = (tf.constant(0), Pair(tf.constant(1), tf.constant(2)))
      c = lambda i, p: i < 10
      b = lambda i, p: (i + 1, Pair((p.j + p.k), (p.j - p.k)))
      ijk_final = tf.while_loop(c, b, ijk_0)
      ```
  
      Example using shape_invariants:
  
      ```python
      i0 = tf.constant(0)
      m0 = tf.ones([2, 2])
      c = lambda i, m: i < 10
      b = lambda i, m: [i+1, tf.concat([m, m], axis=0)]
      tf.while_loop(
          c, b, loop_vars=[i0, m0],
          shape_invariants=[i0.get_shape(), tf.TensorShape([None, 2])])
      ```
  
      Example which demonstrates non-strict semantics: In the following
      example, the final value of the counter `i` does not depend on `x`. So
      the `while_loop` can increment the counter parallel to updates of `x`.
      However, because the loop counter at one loop iteration depends
      on the value at the previous iteration, the loop counter itself cannot
      be incremented in parallel. Hence if we just want the final value of the
      counter (which we print on the line `print(sess.run(i))`), then
      `x` will never be incremented, but the counter will be updated on a
      single thread. Conversely, if we want the value of the output (which we
      print on the line `print(sess.run(out).shape)`), then the counter may be
      incremented on its own thread, while `x` can be incremented in
      parallel on a separate thread. In the extreme case, it is conceivable
      that the thread incrementing the counter runs until completion before
      `x` is incremented even a single time. The only thing that can never
      happen is that the thread updating `x` can never get ahead of the
      counter thread because the thread incrementing `x` depends on the value
      of the counter.
  
      ```python
      import tensorflow as tf
  
      n = 10000
      x = tf.constant(list(range(n)))
      c = lambda i, x: i < n
      b = lambda i, x: (tf.compat.v1.Print(i + 1, [i]), tf.compat.v1.Print(x + 1,
      [i], "x:"))
      i, out = tf.while_loop(c, b, (0, x))
      with tf.compat.v1.Session() as sess:
          print(sess.run(i))  # prints [0] ... [9999]
  
          # The following line may increment the counter and x in parallel.
          # The counter thread may get ahead of the other thread, but not the
          # other way around. So you may see things like
          # [9996] x:[9987]
          # meaning that the counter thread is on iteration 9996,
          # while the other thread is on iteration 9987
          print(sess.run(out).shape)
      ```
      """
      if not callable(cond):
          raise TypeError("'cond' must be callable.")
      if not callable(body):
          raise TypeError("'body' must be callable.")
      if parallel_iterations < 1:
          raise TypeError(
              "'parallel_iterations' must be a positive integer."
          )
  
      loop_vars = variable_utils.convert_variables_to_tensors(loop_vars)
  
      # Always enable control flow v2 if building a function, regardless of toggle.
      executing_eagerly = context.executing_eagerly()
      if (
          util.EnableControlFlowV2(ops.get_default_graph())
          and not executing_eagerly
      ):
          return while_v2.while_loop(
              cond,
              body,
              loop_vars,
              shape_invariants=shape_invariants,
              parallel_iterations=parallel_iterations,
              maximum_iterations=maximum_iterations,
              name=name,
              return_same_structure=return_same_structure,
              back_prop=back_prop,
          )
  
      with ops.name_scope(name, "while", loop_vars):
          if not loop_vars:
              raise ValueError("'loop_vars' must be provided.")
          try_to_pack = (
              len(loop_vars) == 1 and not return_same_structure
          )
          if maximum_iterations is not None:
              maximum_iterations = ops.convert_to_tensor(
                  maximum_iterations, name="maximum_iterations"
              )
              if maximum_iterations.shape.ndims != 0:
                  raise ValueError(
                      "'maximum_iterations' must be a scalar. "
                      f"Received shape: {maximum_iterations.shape}"
                  )
  
              if executing_eagerly:
                  counter = 0
                  maximum_iterations = int(maximum_iterations.numpy())
              else:
                  counter = constant_op.constant(
                      0,
                      dtype=maximum_iterations.dtype,
                      name="iteration_counter",
                  )
              orig_cond = cond
              orig_body = body
              if try_to_pack:
                  loop_vars = (counter, loop_vars[0])
                  cond = (
                      lambda i, lv: (  # pylint: disable=g-long-lambda
                          math_ops.logical_and(
                              i < maximum_iterations, orig_cond(lv)
                          )
                      )
                  )
                  body = lambda i, lv: (i + 1, orig_body(lv))
              else:
                  loop_vars = (counter, loop_vars)
                  cond = (
                      lambda i, lv: (  # pylint: disable=g-long-lambda
                          math_ops.logical_and(
                              i < maximum_iterations, orig_cond(*lv)
                          )
                      )
                  )
                  body = lambda i, lv: (i + 1, orig_body(*lv))
              try_to_pack = False
  
          if executing_eagerly:
              packed = False  # whether the body result was packed into a 1-item tuple
  
              loop_var_structure = nest.map_structure(
                  type_spec.type_spec_from_value, list(loop_vars)
              )
              while cond(*loop_vars):
                  loop_vars = body(*loop_vars)
                  if try_to_pack and not isinstance(
                      loop_vars, (list, tuple)
                  ):
                      packed = True
                      loop_vars = (loop_vars,)
                  nest.assert_same_structure(
                      loop_var_structure, list(loop_vars)
                  )
  
              def convert(x):
                  if isinstance(x, tensor_array_ops.TensorArray):
                      return x
                  return ops.convert_to_tensor(x)
  
              loop_vars = nest.map_structure(
                  convert, loop_vars, expand_composites=True
              )
              if maximum_iterations is not None:
                  return loop_vars[1]
              else:
                  return loop_vars[0] if packed else loop_vars
  
          if shape_invariants is not None:
              if maximum_iterations is not None:
                  shape_invariants = (
                      tensor_shape.TensorShape([]),
                      shape_invariants,
                  )
  
          loop_context = control_flow_ops.WhileContext(
              maximum_iterations=maximum_iterations,
              parallel_iterations=parallel_iterations,
              back_prop=back_prop,
              swap_memory=swap_memory,
          )
          # Only add non-nested loops to the collection. Any nested control flow will
          # be encapsulated in the root context.
          if loop_context.outer_context is None:
              ops.add_to_collection(
                  ops.GraphKeys.WHILE_CONTEXT, loop_context
              )
          result = loop_context.BuildLoop(
              cond,
              body,
              loop_vars,
              shape_invariants,
              return_same_structure,
          )
          if maximum_iterations is not None:
              return result[1]
          else:
              return result
  
file_path: tensorflow/tensorflow/python/ops/while_loop.py
imports: from tensorflow.python.eager import context
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import type_spec
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import control_flow_util
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import tensor_array_ops
  from tensorflow.python.ops import while_v2
  from tensorflow.python.util import deprecation
  from tensorflow.python.util import nest
  from tensorflow.python.util import variable_utils
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 2 for calling a global function with example usage: while_loop(cond, body, loop_vars, shape_invariants, parallel_iterations, back_prop, swap_memory, name, maximum_iterations, return_same_structure) and returns: while_v2, result, result, ops, loop_vars, unknown, x