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;

}

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) {

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,

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.

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.types.KtType
import org.jetbrains.kotlin.name.CallableId
import org.jetbrains.kotlin.name.Name
import org.jetbrains.kotlin.resolve.calls.inference.returnTypeOrNothing
import org.jetbrains.kotlin.resolve.descriptorUtil.isExtension
import org.jetbrains.kotlin.resolve.sam.SamConstructorDescriptor
import org.jetbrains.kotlin.resolve.sam.SamTypeAliasConstructorDescriptor

import org.jetbrains.kotlin.name.CallableId
import org.jetbrains.kotlin.name.Name
import org.jetbrains.kotlin.resolve.DescriptorUtils
import org.jetbrains.kotlin.resolve.calls.inference.returnTypeOrNothing
import org.jetbrains.kotlin.resolve.calls.tasks.isDynamic
import org.jetbrains.kotlin.resolve.descriptorUtil.isExtension

internal class KtFe10DescFunctionSymbol private constructor(

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

  @Override
  public int hashCode() {
    return Sets.hashCodeImpl(this);
  }

  // This declaration is needed to make Set.iterator() and
  // ImmutableCollection.iterator() appear consistent to javac's type inference.
  @Override
  public abstract UnmodifiableIterator<E> iterator();

  abstract static class CachingAsList<E> extends ImmutableSet<E> {
    @LazyInit private transient ImmutableList<E> asList;

    @Override

</p>

<h3 id="Type_inference">Type inference</h3>

<p>
A use of a generic function may omit some or all type arguments if they can be
<i>inferred</i> from the context within which the function is used, including
the constraints of the function's type parameters.
Type inference succeeds if it can infer the missing type arguments