entities.put("fnof", "\u0192");
        entities.put("Alpha", "\u0391");
        entities.put("Beta", "\u0392");
        entities.put("Gamma", "\u0393");
        entities.put("Delta", "\u0394");
        entities.put("Epsilon", "\u0395");
        entities.put("Zeta", "\u0396");
        entities.put("Eta", "\u0397");
        entities.put("Theta", "\u0398");

}

// nttElement is an NTT representation, an element of T_q, represented as an
// array according to FIPS 203 (DRAFT), Section 2.4.
type nttElement [n]fieldElement

// gammas are the values ζ^2BitRev7(i)+1 mod q for each index i.

213C          ; mapped                 ; 03C0          # 4.1  DOUBLE-STRUCK SMALL PI
213D..213E    ; mapped                 ; 03B3          # 3.2  DOUBLE-STRUCK SMALL GAMMA..DOUBLE-STRUCK CAPITAL GAMMA
213F          ; mapped                 ; 03C0          # 3.2  DOUBLE-STRUCK CAPITAL PI
2140          ; mapped                 ; 2211          # 3.2  DOUBLE-STRUCK N-ARY SUMMATION

                                PatternRewriter &rewriter) const override {
    // Mul. Required 1-D rhs for batch normalization.
    DenseElementsAttr gamma_cst;
    Value gamma = mul_op.getRhs();
    if (!matchPattern(gamma, m_Constant(&gamma_cst))) return failure();
    if (gamma_cst.getType().getRank() != 1) return failure();

    // Affine op
    Operation *mul_op_lhs = mul_op.getLhs().getDefiningOp();

                     Thread.sleep(100);
                     org.junit.Assert.assertEquals(1, 1);
                }
                @org.junit.Test public void gamma() throws Exception {
                     Thread.sleep(100);
                     org.junit.Assert.assertEquals(1, 1);
                }
                @org.junit.Test public void delta() throws Exception {

    %t:tensor<1x10x10x3xf32>, %m:tensor<3xf32>, %v:tensor<3xf32>, %beta:tensor<3xf32>, %gamma:tensor<3xf32>) -> (tensor<1x10x10x3xf32>) {
  %0 = "tf.BatchNormWithGlobalNormalization"(%t, %m, %v, %beta, %gamma) {T = "tfdtype$DT_FLOAT", variance_epsilon = 0.001 : f32, scale_after_normalization = false} : (tensor<1x10x10x3xf32>, tensor<3xf32>, tensor<3xf32>, tensor<3xf32>, tensor<3xf32>) -> (tensor<1x10x10x3xf32>)

	"ucirc":    "\u00FB",
	"uuml":     "\u00FC",
	"yacute":   "\u00FD",
	"thorn":    "\u00FE",
	"yuml":     "\u00FF",
	"fnof":     "\u0192",
	"Alpha":    "\u0391",
	"Beta":     "\u0392",
	"Gamma":    "\u0393",
	"Delta":    "\u0394",
	"Epsilon":  "\u0395",
	"Zeta":     "\u0396",
	"Eta":      "\u0397",
	"Theta":    "\u0398",
	"Iota":     "\u0399",
	"Kappa":    "\u039A",
	"Lambda":   "\u039B",

If "scale_after_normalization" is true, this tensor will be multiplied
with the normalized tensor.}]>:$gamma,

    F32Attr:$variance_epsilon,
    BoolAttr:$scale_after_normalization
  );

  let results = (outs

        .body("abcd")
        .build(),
    )
    server.enqueue(
      MockResponse.Builder()
        .addHeader("Transfer-Encoding: none")
        .addHeaderLenient("Gamma", "Γ")
        .addHeaderLenient("Δ", "Delta")
        .code(HttpURLConnection.HTTP_NOT_MODIFIED)
        .build(),
    )
    val response1 = get(server.url("/"))

    * Bir çox proqramlaşdırma dilləri üçün avtomatlaşdırılmış <abbr title="Müştəri: Client">müştəri</abbr> kodu yaratma sistemləri.
* 2 interaktiv sənədləşmə veb interfeysini birbaşa təmin edəcək.

---

Yeni başlamışıq, amma siz artıq işin məntiqini başa düşmüsünüz.

İndi aşağıdakı sətri dəyişdirməyə çalışın:

```Python
    return {"item_name": item.name, "item_id": item_id}
```

...bundan:

```Python