#### Option A: Reference colab notebooks

1)  Reference [TensorFlow Model Colab](https://colab.research.google.com/gist/ymodak/e96a4270b953201d5362c61c1e8b78aa/tensorflow-datasets.ipynb?authuser=1): Demonstrate how to build your TF model.

        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_MEAN);
    assertThat(manyValuesAccumulatorByAddAllStatsAccumulator.mean())
        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_MEAN);
    // For datasets of many double values created from an iterable, we test many combinations of
    // finite and non-finite values:
    for (ManyValues values : ALL_MANY_VALUES) {
      StatsAccumulator accumulator = new StatsAccumulator();

                dstIndex += 4;
                SMBUtil.writeInt4(0, dst, dstIndex);
                dstIndex += 4; // Reserved
                final int dataLen = size8(nc.encode(dst, dstIndex));
                SMBUtil.writeInt2(dataLen, dst, lenOffset);
                dstIndex += dataLen;
            }
        }
        return dstIndex - start;
    }

    /**
     * {@inheritDoc}
     *

        // Test when data length exceeds outputBuffer size
        byte[] buffer = new byte[2000];
        int dataLen = outputBuffer.length + 100; // Exceeds outputBuffer size

        SMBProtocolDecodingException exception =
                assertThrows(SMBProtocolDecodingException.class, () -> response.readDataWireFormat(buffer, 0, dataLen));

        assertEquals("Payload exceeds buffer size", exception.getMessage());
    }

				int64(testCase.dataLen), testCase.chunkSize, bytes.NewReader(testCase.data),
				testCase.accessKey, testCase.secretKey)
		case testCase.contentEncoding == "":
			req, err = newTestStreamingSignedRequest(http.MethodPut,
				getPutObjectURL("", testCase.bucketName, testCase.objectName),
				int64(testCase.dataLen), testCase.chunkSize, bytes.NewReader(testCase.data),

        // Note: This is a rough check - exact performance depends on JVM, hardware, etc.
        // We expect Set lookup to be at least as fast as array lookup for large datasets
        assertTrue("Set-based lookup should be faster or comparable to array-based lookup for large datasets",
                setDuration <= arrayDuration * 2); // Allow 2x margin for test stability
    }

    /**
     * Helper method to create a test FessConfig.

  public static LinearTransformation horizontal(double y) {
    checkArgument(isFinite(y));
    double slope = 0.0;
    return new RegularLinearTransformation(slope, y);
  }

  /**
   * Builds an instance for datasets which contains {@link Double#NaN}. The {@link #isHorizontal}
   * and {@link #isVertical} methods return {@code false} and the {@link #slope}, and {@link

        byte[] dataHmac = getHmac(dataChecksum, codeHmac);
        SecretKeySpec dataKey = new SecretKeySpec(dataHmac, KerberosConstants.RC4_ALGORITHM);

        cipher = Cipher.getInstance(KerberosConstants.RC4_ALGORITHM);
        cipher.init(Cipher.DECRYPT_MODE, dataKey);

        int plainDataLength = data.length - KerberosConstants.CHECKSUM_SIZE;

    assertThat(body!!.contentType().toString()).isEqualTo(
      "application/x-www-form-urlencoded; charset=utf-8",
    )
    assertThat(bodyAsString(body)).isEqualTo("foo")
  }

  @Test
  fun dataPut() {
    val request = fromArgs("-d", "foo", "-X", "PUT", "http://example.com").createRequest()
    val body = request.body
    assertThat(request.method).isEqualTo("PUT")

	type callback struct {
		name    string
		before  string
		after   string
		remove  bool
		replace bool
		err     string
		match   func(*gorm.DB) bool
		h       func(*gorm.DB)
	}

	datas := []struct {
		callbacks []callback
		err       string
		results   []string
	}{
		{
			callbacks: []callback{{h: c1}, {h: c2}, {h: c3}, {h: c4}, {h: c5}},
			results:   []string{"c1", "c2", "c3", "c4", "c5"},
		},