EXPECT_EQ(TF_DOUBLE, static_cast<TF_DataType>(tensorflow::DT_DOUBLE));
  EXPECT_EQ(TF_INT32, static_cast<TF_DataType>(tensorflow::DT_INT32));
  EXPECT_EQ(TF_UINT8, static_cast<TF_DataType>(tensorflow::DT_UINT8));
  EXPECT_EQ(TF_INT16, static_cast<TF_DataType>(tensorflow::DT_INT16));
  EXPECT_EQ(TF_INT8, static_cast<TF_DataType>(tensorflow::DT_INT8));

pkg debug/elf, const DT_CONFIG = 1879047930
pkg debug/elf, const DT_CONFIG DynTag
pkg debug/elf, const DT_DEPAUDIT = 1879047931
pkg debug/elf, const DT_DEPAUDIT DynTag
pkg debug/elf, const DT_FEATURE = 1879047676
pkg debug/elf, const DT_FEATURE DynTag
pkg debug/elf, const DT_FILTER = 2147483647
pkg debug/elf, const DT_FILTER DynTag
pkg debug/elf, const DT_FLAGS_1 = 1879048187
pkg debug/elf, const DT_FLAGS_1 DynTag

	}
	consumed = int(dirent.Reclen)
	if direntInode(dirent) == 0 { // File absent in directory.
		return
	}
	switch dirent.Type {
	case syscall.DT_REG:
		typ = 0
	case syscall.DT_DIR:
		typ = os.ModeDir
	case syscall.DT_LNK:
		typ = os.ModeSymlink
	default:
		// Skip all other file types. Revisit if/when this code needs
		// to handle such files, MinIO is only interested in

inline bool IsDtypeTrainable(DataType dtype) {
  switch (dtype) {
    case DT_HALF:
    case DT_BFLOAT16:
    case DT_FLOAT:
    case DT_DOUBLE:
    case DT_COMPLEX64:
    case DT_COMPLEX128:
    case DT_RESOURCE:
    case DT_VARIANT:
      return true;
    case DT_QINT8:
    case DT_QINT16:
    case DT_QINT32:
    case DT_QUINT8:
    case DT_QUINT16:
      return tensorflow::flags::Global()

      "    signature {"
      "      name: 'AddVariablesFunction'"
      "      input_arg {"
      "        name: 'var'"
      "        type: DT_RESOURCE"
      "      }"
      "      output_arg {"
      "        name: 'sum'"
      "        type: DT_FLOAT"
      "      }"
      "    }"
      "    node_def {"
      "      name: 'read0'"
      "      op: 'ReadVariableOp'"
      "      input: 'var'"

  AbstractTensorHandlePtr x;
  {
    tracing::TracingTensorHandle* x_raw = nullptr;
    PartialTensorShape shape;
    Status s = dyn_cast<tracing::TracingContext>(ctx.get())->AddParameter(
        DT_FLOAT, shape, &x_raw);
    ASSERT_EQ(errors::OK, s.code()) << s.message();
    x.reset(x_raw);
  }

  PartialTensorShape shape;
  Status s = x->Shape(&shape);
  ASSERT_EQ(errors::OK, s.code()) << s.message();

                                }
                                final Date dt = FessFunctions.parseDate(values[0], dateFormate);
                                if (dt != null) {
                                    dataMap.put(mapping.getValue1(), FessFunctions.formatDate(dt));
                                } else {
                                    logger.warn("Failed to parse {}", mapping);

#   MAVEN_DEBUG_ADDRESS (Optional) Set the debug address. Default value is localhost:8000
# -----------------------------------------------------------------------------

MAVEN_DEBUG_OPTS="-Xdebug -Xrunjdwp:transport=dt_socket,server=y,suspend=y,address=${MAVEN_DEBUG_ADDRESS:-localhost:8000}"

echo Preparing to execute Maven in debug mode

  EXPECT_NE(attr_found, attr_values.cend());
  EXPECT_EQ(attr_found->second.list().type(0), tensorflow::DataType::DT_BOOL);
  EXPECT_EQ(attr_found->second.list().type(1), tensorflow::DataType::DT_FLOAT);
  EXPECT_EQ(attr_found->second.list().type(2), tensorflow::DataType::DT_INT32);

  TFE_TensorHandle* retvals[1] = {nullptr};
  int num_retvals = 1;
  TFE_Execute(assertOp, &retvals[0], &num_retvals, status);

                                                 data-validation="custom"
                                                 data-validation-regexp="(^$|^[1-9]\d{3}\-\d\d\-\d\dT\d\d\:\d\d\:\d\d\.\d{3}Z$)"
                                                 data-validation-help="yyyy-MM-dd'T'HH:mm:ss.SSS'Z'"/>
                                    </div>
                                </div>