void PlaceholderHelper(TF_Graph* graph, TF_Status* s, const char* name,
                       TF_DataType dtype, const std::vector<int64_t>& dims,
                       TF_Operation** op) {
  TF_OperationDescription* desc = TF_NewOperation(graph, "Placeholder", name);
  TF_SetAttrType(desc, "dtype", dtype);
  if (!dims.empty()) {
    TF_SetAttrShape(desc, "shape", dims.data(), dims.size());
  }

  absl::Status status;
  if (reader_ != nullptr) {
    status = reader_->GetTensor(name, out_tensor);
  } else {
    tensorflow::DataType dtype;
    tensorflow::TensorShape shape;
    status = v2_reader_->LookupDtypeAndShape(name, &dtype, &shape);
    if (status.ok()) {
      out_tensor->reset(new Tensor(dtype, shape));
      status = v2_reader_->Lookup(name, out_tensor->get());
      if (!status.ok()) out_tensor->reset();
    }
  }

                                                void* data, size_t len,
                                                TF_Status* status) {
  auto dtype = static_cast<tensorflow::DataType>(data_type);
  DCHECK(tensorflow::DataTypeCanUseMemcpy(dtype));

  tensorflow::Tensor tensor(dtype, tensorflow::TensorShape({}));
  std::memcpy(tensorflow::TensorCApi::Buffer(tensor)->data(), data, len);

  status->status = absl::OkStatus();

            metadata.type = TF_ATTR_INT;
          } else if (typestr == "list(float)") {
            metadata.type = TF_ATTR_FLOAT;
          } else if (typestr == "list(bool)") {
            metadata.type = TF_ATTR_BOOL;
          } else if (typestr == "list(type)") {
            metadata.type = TF_ATTR_TYPE;
          } else if (typestr == "list(shape)") {
            metadata.type = TF_ATTR_SHAPE;

        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;

#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";

}

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 signedHeaders, ErrNone
}

// signValues data type represents structured form of AWS Signature V4 header.
type signValues struct {
	Credential    credentialHeader
	SignedHeaders []string
	Signature     string
}

// preSignValues data type represents structured form of AWS Signature V4 query string.
type preSignValues struct {
	signValues
	Date    time.Time
	Expires time.Duration
}

    public void decode(final NdrBuffer buf) throws NdrException {
        decode_header(buf);

        if (this.ptype != 12 && this.ptype != 2 && this.ptype != 3 && this.ptype != 13) {
            throw new NdrException("Unexpected ptype: " + this.ptype);
        }

        if (this.ptype == 2 || this.ptype == 3) { /* Response or Fault */
            this.alloc_hint = buf.dec_ndr_long();