typedef void (*MemoryReleaser)(void* data, size_t len, void* arg);

  // Create a tensor instance from the given data buffer and description.
  // `memory_releaser` will be called on destruction, and it's responsible for
  // cleaning up the underlying buffer.
  virtual AbstractTensorInterface* CreateTensor(
      DataType dtype, const int64_t* dims, int num_dims, void* data, size_t len,

						if _, ok := first[dbName].(string); !ok {
							t.Errorf("invalid data type for %v, got %#v", dbName, first[dbName])
						}
					case "Age":
						if _, ok := first[dbName].(uint); !ok {
							t.Errorf("invalid data type for %v, got %#v", dbName, first[dbName])
						}
					case "Birthday":
						if _, ok := first[dbName].(*time.Time); !ok {
							t.Errorf("invalid data type for %v, got %#v", dbName, first[dbName])
						}
					}

  }

  @AfterEach
  fun after() {
    listener.assertExhausted()
  }

  @Test
  fun processResponse() {
    server.enqueue(
      MockResponse.Builder()
        .body(
          """
          |data: hey
          |
          |
          """.trimMargin(),
        ).setHeader("content-type", "text/event-stream")
        .build(),
    )
    val request =
      Request.Builder()
        .url(server.url("/"))

                                    "code": {
                                        "local": {
                                            "filename": "/var/lib/istio/data/fe482be5ac123d3d387b0c68b9ed7f8ed65824e7d0bbc88a9a9a71dd8391d96c/d8ef3957b4cf09da9ed25e88bc75ac397ea875d88c5cbed356872f936a86d928.wasm"
                                        }
                                    },

[2020-01-01 00:00:00] >> 0x00000000     0 SETTINGS      ACK
[2020-01-01 00:00:00] << 0x00000003   322 HEADERS       END_HEADERS
[2020-01-01 00:00:00] << 0x00000003   288 DATA
[2020-01-01 00:00:00] << 0x00000003     0 DATA          END_STREAM
[2020-01-01 00:00:00] << 0x00000000     8 GOAWAY
[2020-01-01 00:00:05] << 0x00000000     8 GOAWAY
```

### Task Runner Logging 

        assertEquals("binaryTransformer", binaryTransformer.getName());
    }

    public void test_transform() {
        final byte[] data = new String("xyz").getBytes();
        final ResponseData responseData = new ResponseData();
        responseData.setResponseBody(data);
        final ResultData resultData = binaryTransformer.transform(responseData);
        assertEquals("xyz", new String(resultData.getData()));
    }

https://docs.minio.io/docs/distributed-minio-quickstart-guide ## rootUser: "" rootPassword: "" ## Use existing Secret that store following variables: ## ## | Chart var | .data.<key> in Secret | ## |:-------- ## | rootUser | rootUser | ## | rootPassword | rootPassword | ## ## All mentioned variables will be ignored in values file. ## .data.rootUser and .data.rootPassword are mandatory, ## others depend on enabled status of corresponding sections. existingSecret: "" ## Directory on the MinIO pof certsPath: "/etc/minio/certs/"...

// that the file has no data in it, which is rather odd.
//
// As an example, if the underlying raw file contains the 10-byte data:
//
//	var compactFile = "abcdefgh"
//
// And the sparse map has the following entries:
//
//	var spd sparseDatas = []sparseEntry{
//		{Offset: 2,  Length: 5},  // Data fragment for 2..6
//		{Offset: 18, Length: 3},  // Data fragment for 18..20
//	}

/// tip

Het tweede argument van <a href="https://docs.python.org/3.8/library/os.html#os.getenv" class="external-link" target="_blank">`os.getenv()`</a> is de standaardwaarde die wordt geretourneerd.

Als je dit niet meegeeft, is de standaardwaarde `None`. In dit geval gebruiken we standaard `"World"`.

///

Dan zou je dat Python-programma kunnen aanroepen:

//// tab | Linux, macOS, Windows Bash

<div class="termy">

```console

                                                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();