feed3, control_inputs, TF_OperationNumControlInputs(feed3));
  ASSERT_EQ(2, num_control_inputs);
  EXPECT_EQ(feed, control_inputs[0]);
  EXPECT_EQ(feed2, control_inputs[1]);

  // Export to a graph def so we can import a graph with control dependencies
  TF_DeleteBuffer(graph_def);
  graph_def = TF_NewBuffer();
  TF_GraphToGraphDef(graph, graph_def, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

	switch word {
	case "TEXT":
		// Defines text symbol in operands[0].
		if len(operands) > 0 {
			p.start(operands[0])
			if name, abi, ok := p.funcAddress(); ok {
				fmt.Fprintf(w, "def %s %s\n", name, abi)
			}
		}
		return
	case "GLOBL", "PCDATA":
		// No text definitions or symbol references.
	case "DATA", "FUNCDATA":
		// For DATA, operands[0] is defined symbol.

// Fetches any input mappings requested via
// TF_ImportGraphDefOptionsAddInputMapping() that didn't appear in the GraphDef
// and weren't used as input to any node in the imported graph def. The number
// of fetched mappings is returned in `num_missing_unused_input_mappings`. The
// array of each mapping's source node name is returned in `src_names`, and the

    val k1Creator = cache.edit("k1")!!
    k1Creator.setString(0, "AB")
    k1Creator.setString(1, "C")
    k1Creator.commit()
    val k2Creator = cache.edit("k2")!!
    k2Creator.setString(0, "DEF")
    k2Creator.setString(1, "G")
    k2Creator.commit()
    val k1Snapshot = cache["k1"]!!
    k1Snapshot.close()
    cache.close()

使い方はとても簡単です。例えば、`GET`リクエストを実行するには、このように書けば良いです:

```Python
response = requests.get("http://example.com/some/url")
```

対応するFastAPIのパスオペレーションはこのようになります:

```Python hl_lines="1"
@app.get("/some/url")
def read_url():
    return {"message": "Hello World"}
```

 `requests.get(...)` と`@app.get(...)` には類似点が見受けられます。

/// check | **FastAPI**へ与えたインスピレーション

* シンプルで直感的なAPIを持っている点。
* HTTPメソッド名を直接利用し、単純で直感的である。

		//
		// import org.apache.spark.sql.SparkSession
		// import org.apache.spark.sql.types.{IntegerType, StringType, StructField, StructType}
		// object SparkJSONRead {
		//   def main(args: Array[String]): Unit = {
		//     val spark:SparkSession = SparkSession.builder()
		//       .appName("SparkByExample")
		//       .master("local[1]").getOrCreate()
		//

        StandardizedConv2D(tf.keras.layers.Conv2D): def call(self, inputs):
        mean, var = tf.nn.moments(self.kernel, axes=[0, 1, 2], keepdims=True)
        return self.convolution_op(inputs, (self.kernel - mean) / tf.sqrt(var +
        1e-10))` Alternatively, you can override `convolution_op`: `python class
        StandardizedConv2D(tf.keras.Layer): def convolution_op(self, inputs,

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    * Example:
      ```yaml
      kind: ConfigMap
      apiVersion: v1
      metadata:
        name: kube-dns
        namespace: kube-system
      data:
        federations: abc=def
      ```
* azure: support multiple ipconfigs on a NIC ([#36841](https://github.com/kubernetes/kubernetes/pull/36841), [@colemickens](https://github.com/colemickens))

ddnss.org
dds
de
de.com
de.cool
de.eu.org
de.trendhosting.cloud
de.us
deal
dealer
deals
deatnu.no
debian.net
deca.jp
deci.jp
dedibox.fr
dedyn.io
def.br
definima.io
definima.net
degree
delhi.in
delivery
dell
dell-ogliastra.it
dellogliastra.it
deloitte
delta
demo.datacenter.fi
demo.datadetect.com
demo.jelastic.com