using tensorflow::NodeDef;
  using tensorflow::OpRegistrationData;
  using tensorflow::Tensor;
  using tensorflow::shape_inference::DimensionHandle;
  using tensorflow::shape_inference::InferenceContext;
  using tensorflow::shape_inference::ShapeAndType;
  using tensorflow::shape_inference::ShapeHandle;

  const int num_inputs = input_shapes->num_items;
  NodeDef node_def;

// `shape_inference::InferenceContext` constructor. Note the following:
//   - The inputs of the `op` are not used for shape inference. So, it is
//     OK to not have the inputs properly set in `op`. See `input_tensors`
//     if you want shape inference to consider the input tensors of the
//     op for shape inference.
//   - The types need not be set in `input_shapes` as it is not used.

namespace tensorflow {
namespace tracing {
namespace graph {

class GraphContext;
class GraphOperation;
class GraphTensor;

auto& kUnknownDim = shape_inference::InferenceContext::kUnknownDim;
auto& kUnknownRank = shape_inference::InferenceContext::kUnknownRank;

// GraphTensor wraps a `TF_Output`, i.e. a pointer to TF_Operation and the index
// into the list of outputs for the operation.

func (v *Value) InferBytesType() (err error) {
	b, ok := v.ToBytes()
	if !ok {
		return fmt.Errorf("InferByteType: Input is not bytes, but %v", v.GetTypeString())
	}

	// Check for numeric inference
	if x, ok := v.bytesToInt(); ok {
		v.setInt(x)
		return nil
	}
	if x, ok := v.bytesToFloat(); ok {
		v.setFloat(x)
		return nil
	}
	if x, ok := v.bytesToBool(); ok {
		v.setBool(x)

import org.jetbrains.kotlin.name.StandardClassIds
import org.jetbrains.kotlin.renderer.DescriptorRenderer
import org.jetbrains.kotlin.renderer.render
import org.jetbrains.kotlin.resolve.calls.inference.CapturedType
import org.jetbrains.kotlin.resolve.descriptorUtil.annotationClass
import org.jetbrains.kotlin.types.*
import org.jetbrains.kotlin.types.checker.NewCapturedType

  }

  ~ShapeInferenceTest() override {
    TFE_DeleteContextOptions(tfe_context_options_);
    TFE_DeleteContext(tfe_context_);
    TF_DeleteStatus(status_);
  }

  // Checks the expected result of shape inference for the given `op`.
  void CheckOutputShapes(
      TFE_Op* op,
      const std::vector<absl::optional<std::vector<int64_t>>>& input_shapes_vec,
      const std::vector<TF_Tensor*>& input_tensors,

// If assume_valid_feeds is true, it can help infer shapes in the fanout of fed
// nodes. This may cause incorrectness in graph analyses, but is useful for
// simulation or scheduling.
// If aggressive_shape_inference is true, nodes are executed on the host to
// identify output values when possible and does other aggressive strategies.
// This may cause incorrectness in graph analyses, but is useful for simulation
// or scheduling.

      graph_properties);
}

void TF_InferStatically(TF_GraphProperties* graph_properties,
                        TF_Bool assume_valid_feeds,
                        TF_Bool aggressive_shape_inference,
                        TF_Bool include_input_tensor_values,
                        TF_Bool include_output_tensor_values,
                        TF_Status* status) {
  TF_SetStatus(status, TF_OK, "");

import org.jetbrains.kotlin.psi.*
import org.jetbrains.kotlin.psi.psiUtil.*
import org.jetbrains.kotlin.resolve.BindingContext
import org.jetbrains.kotlin.resolve.calls.components.isVararg
import org.jetbrains.kotlin.resolve.calls.inference.returnTypeOrNothing
import org.jetbrains.kotlin.resolve.calls.smartcasts.MultipleSmartCasts
import org.jetbrains.kotlin.resolve.calls.smartcasts.SingleSmartCast
import org.jetbrains.kotlin.resolve.calls.util.getParameterForArgument

import org.jetbrains.kotlin.psi.psiUtil.getParentOfType
import org.jetbrains.kotlin.resolve.BindingContext
import org.jetbrains.kotlin.resolve.DescriptorUtils
import org.jetbrains.kotlin.resolve.calls.inference.CapturedType
import org.jetbrains.kotlin.resolve.calls.util.getResolvedCall
import org.jetbrains.kotlin.resolve.scopes.utils.getImplicitReceiversHierarchy
import org.jetbrains.kotlin.types.*