tf11 = tf1 / 0 /* ERROR "division by zero" */
	tf12 = tf3 /* ERROR "mismatched types" */ / tf0

	tf16 = tf2 /* ERROR "mismatched types" */ & tf3
	tf17 = tf2 /* ERROR "mismatched types" */ | tf3
	tf18 = tf2 /* ERROR "mismatched types" */ ^ tf3

	// complex values
	tc0 = 0.i
	tc1 = 1.i
	tc2 = 4.2e1i
	tc3 = 3.141592653589793238462643383279502884197169399375105820974944592307816406286i
	tc4 = 1e-1i

)
```

When this option is enabled, exported SavedModels with proto size > 2GB will
automatically save with the new format (`.cpb` instead of `.pb`).

<!-- **Compatibility** -->

The official TF APIs (TF1/TF2 python or C++ loading) have already been
integrated to handle the new format, but some downstream converters may not

from tensorflow.python.util.lazy_loader import KerasLazyLoader as _KerasLazyLoader

# Make sure code inside the TensorFlow codebase can use tf2.enabled() at import.
_os.environ["TF2_BEHAVIOR"] = "1"
from tensorflow.python import tf2 as _tf2
_tf2.enable()

# API IMPORTS PLACEHOLDER

# WRAPPER_PLACEHOLDER

# Make sure directory containing top level submodules is in

"""Serves as a common "main" function for all the SavedModel tests.

There is a fair amount of setup needed to initialize tensorflow and get it
into a proper TF2 execution mode. This hides that boilerplate.
"""

import tempfile

from absl import app
from absl import flags
from absl import logging
import tensorflow.compat.v2 as tf

var _ = F2 /* ERROR "impossible type assertion: F2().(*X2)\n\t*X2 does not implement T2 (missing method M)" */ ().(*X2)

type X2 struct{}

func _() {
	switch F2().(type) {
	case * /* ERROR "impossible type switch case: *X2\n\tF2() (value of type T2) cannot have dynamic type *X2 (missing method M)" */ X2:
	}

  // to populate `MetaInfoDef`s `function_aliases` when the quantized model is
  // exported to the saved model. This field is usually only populated for the
  // TF2 models.
  map<string, string> function_aliases = 6;

  // Holds information about the asset files used for the model. It essentially
  // associates asset file names with the tensors to which the asset file names

}

void CreateReplicatedClusteringPipeline(OpPassManager &pm,
                                        llvm::StringRef module_name) {
  // Since the internal bridge clustering passes are shared among TF1/TF2
  // TF2-only passes should go here. However, this should be very rare and
  // new passes generally should go into the internal
  // AddReplicatedBridgeClusteringPipelinePasses.

namespace tensorflow {
namespace internal {

// Executes a VarHandleOp using `ctx`, and fills `handle` with the DT_RESOURCE
// TensorHandle associated with the variable. This is equivalent to creating an
// unitialized TF2 tf.Variable.
// https://github.com/tensorflow/tensorflow/blob/516608035f85cec8b126712b0ff8407220206b22/tensorflow/python/ops/resource_variable_ops.py#L1867-L1872
Status CreateUninitializedResourceVariable(ImmediateExecutionContext* ctx,

  return Status();
}

Status FlatTensorFunction::MakeCallOp(
    absl::Span<AbstractTensorHandle* const> inputs, ImmediateOpPtr* out) const {
  out->reset(ctx_->CreateOperation());
  // In eager mode, TF2 python executes functions by constructing an op with
  // the name of the functiondef:
  // https://github.com/tensorflow/tensorflow/blob/66668ec0ca432e2f38a575b814f45b6d299d01ed/tensorflow/python/eager/function.py#L545

// enabled or disabled, it will decide whether or not to run the bridge.
//
// The config_proto param is a required input for all TF1 graphs but it is
// redundant for TF2 graphs.
// If getting rollout policy involves graph analysis, `record_stats` is used
// to decide whether to emit metrics on unsupported features of the graph.
MlirBridgeRolloutPolicy GetMlirBridgeRolloutPolicy(