“parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and...

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)

inspected and debugged and it is intended to be used during the development
phase.

As part of the differences that make Eager mode easier to debug, the [shape
inference
functions](https://www.tensorflow.org/guide/create_op#define_the_op_interface)
are skipped, and any checks implemented inside the shape inference code are not
executed.

The security impact of skipping those checks should be low, since the attack

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(

            }
        else firFunctionSymbol.valueParameters[valueParameterReference.parameterIndex]
    )

    // 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
    private inline fun <reified T> ConeContractDescriptionElement.accept() =

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.

struct TF_Library {
  void* lib_handle;
  TF_Buffer op_list;
};

struct TF_Graph {
  TF_Graph();

  mutable tensorflow::mutex mu;
  tensorflow::Graph graph TF_GUARDED_BY(mu);

  // Runs shape inference.
  tensorflow::ShapeRefiner refiner TF_GUARDED_BY(mu);

  // Maps from name of an operation to the Node* in 'graph'.
  std::unordered_map<tensorflow::string, tensorflow::Node*> name_map
      TF_GUARDED_BY(mu);

      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, "");