t.Fatalf("%+v: expected = %d, got: %d", testCase, testCase.expectedIndex, index)
		}
	}
}

// Test objectPartIndex(). generates a sample FileInfo data and asserts
// the output of objectPartIndex() with the expected value.
func TestObjectPartIndex(t *testing.T) {
	testCases := []struct {
		partNum       int
		expectedIndex int
	}{
		{2, 1},

        }
    }

    @Test
    public void searchTestWithBoost() throws Exception {
        String field = "content";
        String query1 = "java";
        String query2 = "sample";
        Map<String, String> params = new HashMap<>();
        params.put("q", field + ":" + query1 + "^1000 OR " + field + ":" + query2);
        params.put("num", "100");

                  return i;
                }
              }
              throw new IllegalArgumentException(
                  "Map.values generator can order only sample values");
            }
          });
      return insertionOrder;
    }

    @Override
    public OneSizeTestContainerGenerator<Map<K, V>, Entry<K, V>> getInnerGenerator() {
      return mapGenerator;

Following is a sample directory structure for MinIO server with TLS certificates.

```sh
$ mc tree --files ~/.minio
/home/user1/.minio
└─ certs
   ├─ CAs
   ├─ private.key
   └─ public.crt
```

 *       at this goal. Important note: it may be easy to achieve the theoretical minimum collision
 *       rate when using completely <i>random</i> sample input. The true test of a hash function is
 *       how it performs on representative real-world data, which tends to contain many hidden
 *       patterns and clumps. The goal of a good hash function is to stamp these patterns out as

   * @throws IllegalStateException if the dataset is empty
   */
  public double populationCovariance() {
    checkState(count() != 0);
    return sumOfProductsOfDeltas / count();
  }

  /**
   * Returns the sample covariance of the values. The count must be greater than one.
   *
   * <p>This is not guaranteed to return zero when the dataset consists of the same pair of values
   * multiple times, due to numerical errors.
   *

 *       at this goal. Important note: it may be easy to achieve the theoretical minimum collision
 *       rate when using completely <i>random</i> sample input. The true test of a hash function is
 *       how it performs on representative real-world data, which tends to contain many hidden
 *       patterns and clumps. The goal of a good hash function is to stamp these patterns out as

    sequences.add(new StringBuffer("henry"));
    sequences.add(new StringBuilder("apple"));
    sequences.add("lemon");

    assertEquals(3, Iterables.indexOf(sequences, STARTSWITH_A));
  }

  public void testIndexOf_genericPredicate2() {
    List<String> sequences = Lists.newArrayList("bob", "charlie", "henry", "apple", "lemon");
    assertEquals(3, Iterables.indexOf(sequences, STARTSWITH_A));
  }

func getXLMetaBytes(totalParts int) []byte {
	xlSampleMeta := getSampleXLMeta(totalParts)
	xlMetaBytes, err := json.Marshal(xlSampleMeta)
	if err != nil {
		panic(err)
	}
	return xlMetaBytes
}

// Returns sample xlMetaV1Object{} for number of parts.
func getSampleXLMeta(totalParts int) xlMetaV1Object {
	xlMeta := newTestXLMetaV1()
	// Number of checksum info == total parts.
	xlMeta.Erasure.Checksums = make([]ChecksumInfo, totalParts)

_TF_WORKSPACE_ROOT = ''
_TF_BAZELRC = ''
_TF_CURRENT_BAZEL_VERSION = None

NCCL_LIB_PATHS = [
    'lib64/', 'lib/powerpc64le-linux-gnu/', 'lib/x86_64-linux-gnu/', ''
]

# List of files to configure when building Bazel on Apple platforms.
APPLE_BAZEL_FILES = [
    'tensorflow/lite/ios/BUILD', 'tensorflow/lite/objc/BUILD',
    'tensorflow/lite/swift/BUILD',
    'tensorflow/lite/tools/benchmark/experimental/ios/BUILD'
]