code: def set_optimizer_experimental_options(options):
      """Set experimental optimizer options.
  
      Note that optimizations are only applied in graph mode, (within tf.function).
      In addition, as these are experimental options, the list is subject to change.
  
      Args:
        options: Dictionary of experimental optimizer options to configure.
          Valid keys:
          - layout_optimizer: Optimize tensor layouts e.g. This will try to use NCHW
            layout on GPU which is faster.
          - constant_folding: Fold constants Statically infer the value of tensors
            when possible, and materialize the result using constants.
          - shape_optimization: Simplify computations made on shapes.
          - remapping: Remap subgraphs onto more efficient implementations.
          - arithmetic_optimization: Simplify arithmetic ops with common
            sub-expression elimination and arithmetic simplification.
          - dependency_optimization: Control dependency optimizations. Remove
            redundant control dependencies, which may enable other optimization.
            This optimizer is also essential for pruning Identity and NoOp nodes.
          - loop_optimization: Loop optimizations.
          - function_optimization: Function optimizations and inlining.
          - debug_stripper: Strips debug-related nodes from the graph.
          - disable_model_pruning: Disable removal of unnecessary ops from the graph
          - scoped_allocator_optimization: Try to allocate some independent Op
            outputs contiguously in order to merge or eliminate downstream Ops.
          - pin_to_host_optimization: Force small ops onto the CPU.
          - implementation_selector: Enable the swap of kernel implementations based
            on the device placement.
          - auto_mixed_precision: Change certain float32 ops to float16 on Volta
            GPUs and above. Without the use of loss scaling, this can cause
            numerical underflow (see
            `keras.mixed_precision.experimental.LossScaleOptimizer`).
          - disable_meta_optimizer: Disable the entire meta optimizer.
          - min_graph_nodes: The minimum number of nodes in a graph to optimizer.
            For smaller graphs, optimization is skipped.
          - auto_parallel: Automatically parallelizes graphs by splitting along
            the batch dimension
      """
      context.context().set_optimizer_experimental_options(options)
  
imports: from typing import Union
  from tensorflow.python.eager import context
  from tensorflow.python.framework import errors
  from tensorflow.python.util import _pywrap_determinism
  from tensorflow.python.util import _pywrap_tensor_float_32_execution
  from tensorflow.python.util import deprecation
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 10 for calling a global function with example usage: set_optimizer_experimental_options(options) without return types