assertNotNull(caught);
            assertEquals(originalException, caught.getCause());
            assertTrue(caught.getMessage().contains("ServletException"));
        } catch (Exception e) {
            fail("Should have caught ServletRuntimeException");
        }
    }

    public void test_serialization() {
        // Test that the serialVersionUID is properly defined

        // However, we'll try to read it safely for compatibility
        if (this.byteCount > bufferIndex - start) {
            try {
                // Attempt to read nativeFileSystem, but don't fail if there are issues
                final int fsLen = this.byteCount - (bufferIndex - start);
                if (fsLen > 0 && fsLen < 256) { // Sanity check on length

			secretKey:          credentials.SecretKey,
			expectedRespStatus: http.StatusLengthRequired,
		},
		// Test case - 4.
		// setting the readSeeker to `nil`, bucket policy parser will fail.
		{
			bucketName:         bucketName,
			bucketPolicyReader: nil,

			policyLen:          10,
			accessKey:          credentials.AccessKey,
			secretKey:          credentials.SecretKey,

            assertTrue("Constructor with required parameters exists", true);
        } catch (final NoSuchMethodException e) {
            fail("Constructor with RequestManager, UserMessagesCreator, and Class[] parameters should exist");
        }
    }

    public void test_classStructure() {
        // Verify the class is generic

        super.setUp();
        fileTypeHelper = new FileTypeHelper();
    }

    public void test_init() {
        try {
            fileTypeHelper.init();
        } catch (Exception e) {
            fail("init() should not throw an exception: " + e.getMessage());
        }
    }

    public void test_add() {
        fileTypeHelper.add("application/pdf", "pdf");

   
   
   *TensorFlow release binaries version 1.6 and higher are prebuilt with AVX instruction sets.*
   
   
   Therefore on any CPU that does not have these instruction sets, either CPU or GPU version of TF will fail to load.
   
   Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:
   
      * Try Google Colab to use TensorFlow.

      fail("Expected IllegalArgumentException");
    } catch (IllegalArgumentException expected) {
    }

    try {
      multiset.add(entry.getElement());
      fail("Expected IllegalArgumentException");
    } catch (IllegalArgumentException expected) {
    }

    try {
      multiset.addAll(singletonList(entry.getElement()));
      fail("Expected IllegalArgumentException");

      for (int q : NONZERO_INTEGER_CANDIDATES) {
        for (RoundingMode mode : ALL_SAFE_ROUNDING_MODES) {
          // Skip some tests that fail due to GWT's non-compliant int implementation.
          // TODO(cpovirk): does this test fail for only some rounding modes or for all?
          if (p == -2147483648 && q == -1 && intsCanGoOutOfRange()) {
            continue;
          }
          int expected =

      range.lowerEndpoint();
      fail();
    } catch (IllegalStateException expected) {
    }
    try {
      range.lowerBoundType();
      fail();
    } catch (IllegalStateException expected) {
    }
  }

  private static void assertUnboundedAbove(Range<Integer> range) {
    assertFalse(range.hasUpperBound());
    try {
      range.upperEndpoint();
      fail();

    try {
      entrySet.add((Entry<K, V>) entryToAdd);
      fail("Expected UnsupportedOperationException or NullPointerException.");
    } catch (UnsupportedOperationException | NullPointerException e) {
      // Expected.
    }
    assertInvariants(map);

    try {
      entrySet.addAll(singleton((Entry<K, V>) entryToAdd));
      fail("Expected UnsupportedOperationException or NullPointerException.");