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;

import org.jetbrains.kotlin.analysis.api.types.KtCapturedType
import org.jetbrains.kotlin.analysis.api.types.KtTypeNullability
import org.jetbrains.kotlin.resolve.calls.inference.CapturedType

internal class KtFe10CapturedType(
    override val fe10Type: CapturedType,
    override val analysisContext: Fe10AnalysisContext
) : KtCapturedType(), KtFe10Type {

}

namespace {

// Helper method that creates a shape handle for a shape described by dims.
tensorflow::shape_inference::ShapeHandle ShapeHandleFromDims(
    tensorflow::shape_inference::InferenceContext* ic, int num_dims,
    const int64_t* dims) {
  if (num_dims != -1) {
    std::vector<tensorflow::shape_inference::DimensionHandle> dim_vec;
    dim_vec.reserve(num_dims);
    for (int i = 0; i < num_dims; ++i) {

### 2. Code

Provide code to help us reproduce your issues using one of the following options:

#### Option A: Reference colab notebooks

1)  Reference [TensorFlow Model Colab](https://colab.research.google.com/gist/ymodak/e96a4270b953201d5362c61c1e8b78aa/tensorflow-datasets.ipynb?authuser=1): Demonstrate how to build your TF model.

// `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.

import org.jetbrains.kotlin.descriptors.PropertyGetterDescriptor
import org.jetbrains.kotlin.descriptors.hasBody
import org.jetbrains.kotlin.name.CallableId
import org.jetbrains.kotlin.resolve.calls.inference.returnTypeOrNothing

internal class KtFe10DescPropertyGetterSymbol(
    override val descriptor: PropertyGetterDescriptor,
    override val analysisContext: Fe10AnalysisContext

import org.jetbrains.kotlin.analysis.api.symbols.pointers.KtSymbolPointer
import org.jetbrains.kotlin.analysis.api.types.KtType
import org.jetbrains.kotlin.descriptors.FunctionDescriptor
import org.jetbrains.kotlin.resolve.calls.inference.returnTypeOrNothing
import org.jetbrains.kotlin.resolve.descriptorUtil.isExtension

internal class KtFe10DescAnonymousFunctionSymbol(
    override val descriptor: FunctionDescriptor,

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)

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.

        constructor: (KtParameterSymbol) -> T
    ): T = constructor(variableReference.descriptor.toKtSymbol(analysisContext) as KtParameterSymbol)

    // Util function to avoid hard coding names of the classes. Type inference will do a better job figuring out the best type to cast to.
    // This visitor isn't type-safe anyway