std::vector<tensorflow::DataType> tensor_dtypes(tensors.size());
  for (int i = 0; i < tensors.size(); i++) {
    tensor_ids[i] = ToId(tensors[i]);
    tensor_dtypes[i] = tensors[i]->DataType();
  }
  return GradientTape::ShouldRecord(tensor_ids, tensor_dtypes);
}
void Tape::DeleteTrace(const AbstractTensorHandle* t) {
  GradientTape::DeleteTrace(ToId(t));
}

std::vector<int64_t> MakeTensorIDList(

  // Summarizes the value of this tensor. The caller takes ownership of the
  // returned buffer. If `status` is not TF_OK, instead returns a null pointer.
  //
  // Does not include the shape and dtype of the tensor (which is generally
  // appended later), but should include any information specific to this custom
  // device which would be useful for debugging.
  //

  handle_methods.deallocator = &ParallelTensorDeallocator;
  handle_methods.summarize = &ParallelTensorSummarize;
  return TensorHandlePtr(TFE_NewCustomDeviceTensorHandle(
      context, parallel_device_name.c_str(), t_released->dtype(), t_released,
      handle_methods, status));
}

// For TFE_CustomDevice::copy_tensor_to_device in the parallel device
// registration.
//
// Since this function is used to satisfy the TFE_CustomDevice C API,

        the layer's dtype. If you had a model that used `float64`, it will
        probably silently use `float32` in TensorFlow2, and a warning will be
        issued that starts with Layer "layer-name" is casting an input tensor
        from dtype float64 to the layer's dtype of float32. To fix, either set
        the default dtype to float64 with

  TF_OperationDescription* init(string type) {
    // Construct op_name to match the name used by REGISTER_OP in the
    // ATTR_TEST_REGISTER calls above.
    string op_name = "CApiAttributesTestOp";
    if (type.find("list(") == 0) {
      op_name += "List";
      type = type.replace(0, 5, "");
      type = type.replace(type.size() - 1, 1, "");
    }
    op_name += type;
    return TF_NewOperation(

  for (int device_index = 0; device_index < TF_DeviceListCount(devices.get());
       ++device_index) {
    std::string device_type =
        TF_DeviceListType(devices.get(), device_index, status.get());
    ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
    if (device_type == "TPU") {
      has_tpu = true;
      break;
    }
  }
  if (has_tpu) {
    BasicTestsForTwoDevices(context.get(),

}

func reqSignatureV4Verify(r *http.Request, region string, stype serviceType) (s3Error APIErrorCode) {
	sha256sum := getContentSha256Cksum(r, stype)
	switch {
	case isRequestSignatureV4(r):
		return doesSignatureMatch(sha256sum, r, region, stype)
	case isRequestPresignedSignatureV4(r):
		return doesPresignedSignatureMatch(sha256sum, r, region, stype)
	default:
		return ErrAccessDenied
	}
}

			return iAddr < jAddr
		}
		iType := retrieveListenerType(verifiedListeners[i])
		jType := retrieveListenerType(verifiedListeners[j])
		return iType < jType
	})

	printStr := "ADDRESSES\tPORT"
	if includeConfigType {
		printStr = "NAME\t" + printStr
	}
	if filter.Verbose {
		printStr += "\tMATCH\tDESTINATION"
	} else {
		printStr += "\tTYPE"
	}
	fmt.Fprintln(w, printStr)

#include "tensorflow/core/platform/strcat.h"
#include "tensorflow/core/platform/test.h"

namespace tensorflow {
namespace {

// Specification for expected input/output and its type.
// DataType value of DT_INVALID signifies that we don't want to
// check the data type.
typedef std::pair<string, DataType> IOSpec;

const char* kFeedStackToString = "File \"feed.cc\", line 10, in alpha";

import java.io.IOException;

import jcifs.internal.dtyp.ACE;
import jcifs.internal.dtyp.SecurityDescriptor;


@SuppressWarnings ( "javadoc" )
public class MsrpcShareGetInfo extends srvsvc.ShareGetInfo {

    public MsrpcShareGetInfo ( String server, String sharename ) {
        super(server, sharename, 502, new srvsvc.ShareInfo502());
        this.ptype = 0;
        this.flags = DCERPC_FIRST_FRAG | DCERPC_LAST_FRAG;
    }