if (h == nullptr) {
    status->status = tensorflow::errors::InvalidArgument("Invalid handle");
    return -1;
  }

  int64_t dim = -1;
  status->status = tensorflow::unwrap(h)->Dim(dim_index, &dim);
  return dim;
}

const char* TFE_TensorHandleDeviceName(TFE_TensorHandle* h, TF_Status* status) {
  if (h == nullptr) {

	lim := event.NewerNoncurrentVersions
	if lim == 0 || len(fivs) <= lim+1 { // fewer than lim _noncurrent_ versions
		for _, fi := range fivs {
			objectInfos = append(objectInfos, fi.ToObjectInfo(i.bucket, i.objectPath(), versioned))
		}
		return objectInfos, nil
	}

	overflowVersions := fivs[lim+1:]

     *            文字列
     * @param delim
     *            分割するためのデリミタ
     * @return 分割された文字列の配列
     */
    public static String[] split(final String str, final String delim) {
        if (isEmpty(str)) {
            return EMPTY_STRINGS;
        }
        final List<String> list = newArrayList();
        final StringTokenizer st = new StringTokenizer(str, delim);
        while (st.hasMoreElements()) {

`,
			totalFields: 308*2 + 1,
		}, {
			file:            "nyc-taxi-data-100k-single-delim.csv",
			recordDelimiter: "^",
			fieldDelimiter:  ",",
			header:          true,

      0;  // TF doesn't handle the strides and byte_offsets here
  return static_cast<void*>(dlm_tensor);
}

TFE_TensorHandle* TFE_HandleFromDLPack(void* dlm, TF_Status* status,
                                       TFE_Context* ctx) {
  DLManagedTensor* dlmt = static_cast<DLManagedTensor*>(dlm);
  DLTensor* dl_tensor = &dlmt->dl_tensor;
  absl::optional<std::string> device_name =
      DeviceNameFromDlContext(dl_tensor->device, status);

					if v != byte(l+i) {
						t.Fatalf("in %d,%d: Next(%d)[%d] = %d, want %d", i, j, k, l, v, l+i)
					}
				}
			}
		}
	}
}

var readBytesTests = []struct {
	buffer   string
	delim    byte
	expected []string
	err      error
}{
	{"", 0, []string{""}, io.EOF},
	{"a\x00", 0, []string{"a\x00"}, nil},
	{"abbbaaaba", 'b', []string{"ab", "b", "b", "aaab"}, nil},

    std::unique_ptr<LoggedTensor> t, TF_Status* status) {
  auto dtype = TFE_TensorHandleDataType(t->tensor);
  TFE_CustomDeviceTensorHandleMethods handle_methods;
  handle_methods.num_dims = &LoggedTensorNumDims;
  handle_methods.dim = &LoggedTensorDim;
  handle_methods.deallocator = &LoggedTensorDeallocator;
  return TFE_NewCustomDeviceTensorHandle(context, logging_device_name.c_str(),
                                         dtype, t.release(), handle_methods,

  ASSERT_EQ(num_dims, -1);
  ASSERT_EQ("Invalid handle", string(TF_Message(status.get())));

  TF_SetStatus(status.get(), TF_OK, "");

  int dim = TFE_TensorHandleDim(h, 0, status.get());
  ASSERT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(status.get()));
  ASSERT_EQ(dim, -1);
  ASSERT_EQ("Invalid handle", string(TF_Message(status.get())));
}

TEST(CAPI, TensorHandleDevices) {

ction(c){var d=!1;e.valAttr("dimension")&&(d=a.formUtils.checkImageDimension(c,e.valAttr("dimension"),h)),!d&&e.valAttr("ratio")&&(d=a.formUtils.checkImageRatio(c,e.valAttr("ratio"),h)),d?(j.errorMessage=h.wrongFileDim+" "+e.valAttr("has-not-valid-dim"),b(!1)):b(!0)},function(a){throw a})}else b(!0)},errorMessage:"",errorMessageKey:""}),a(b).one("validatorsLoaded formValidationSetup",function(b,c,d){var e;e=c?c.find('input[type="file"]'):a('input[type="file"]'),a.formUtils.dialogs.removeInputSty...

// Information about the shape of a Tensor and its type.
struct TF_ShapeAndType {
  // Number of dimensions. -1 indicates unknown rank.
  int num_dims;
  // Array of dimensions. -1 indicates unknown dim.
  int64_t* dims;
  // The data type. May be 0 to denote unknown type.
  TF_DataType dtype;
};

typedef struct TF_ShapeAndType TF_ShapeAndType;

// A list of TF_ShapeAndType elements..