/*
     * Open the file or fail if it does not exist
     * aka OPEN_EXISTING
     */

    static final int FILE_OPEN = 0x1;

    /*
     * Create the file or fail if it does not exist
     * aka CREATE_NEW
     */

    static final int FILE_CREATE = 0x2;

    /*
     * Open the file or create it if it does not exist
     * aka OPEN_ALWAYS
     */

    )

    assert fixed_content == expected_content  # Translated doc remains unchanged
    assert "Error processing docs/lang/docs/doc.md" in result.output
    assert (
        "Number of code include placeholders does not match the number of code includes "
        "in the original document (4 vs 3)"
    ) in result.output


@pytest.mark.parametrize(
    "copy_test_files",

}

// BucketRemoteDestinationNotFound bucket does not exist.
type BucketRemoteDestinationNotFound GenericError

func (e BucketRemoteDestinationNotFound) Error() string {
	return "Destination bucket does not exist: " + e.Bucket
}

// BucketRemoteTargetNotFound remote target does not exist.
type BucketRemoteTargetNotFound GenericError

 * static final} constants.
 *
 * {@snippet :
 * // Bad! Do not do this!
 * Joiner joiner = Joiner.on(',');
 * joiner.skipNulls(); // does nothing!
 * return joiner.join("wrong", null, "wrong");
 * }
 *
 * <p>See the Guava User Guide article on <a
 * href="https://github.com/google/guava/wiki/StringsExplained#joiner">{@code Joiner}</a>.
 *

		Err:     "key with given key ID already exits",
	}

	// ErrKeyNotFound is an error returned by the KMS when trying to
	// use a key that does not exist.
	ErrKeyNotFound = Error{
		Code:    http.StatusNotFound,
		APICode: "kms:KeyNotFound",
		Err:     "key with given key ID does not exist",
	}

	// ErrDecrypt is an error returned by the KMS when the decryption
	// of a ciphertext failed.
	ErrDecrypt = Error{

   
   
   *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.

   * href="http://en.wikipedia.org/wiki/Memoization">memoization</a>
   *
   * <p>The returned supplier is thread-safe. The supplier's serialized form does not contain the
   * cached value, which will be recalculated when {@code get()} is called on the reserialized
   * instance. The actual memoization does not happen when the underlying delegate throws an
   * exception.
   *

          long topSize) {
    checkArgument(
        (topCharacteristics & Spliterator.SUBSIZED) == 0,
        "flatMap does not support SUBSIZED characteristic");
    checkArgument(
        (topCharacteristics & Spliterator.SORTED) == 0,
        "flatMap does not support SORTED characteristic");
    checkNotNull(fromSpliterator);
    checkNotNull(function);
    return new FlatMapSpliteratorOfObject<>(

# Is development environment here? (used for various purpose, you should set false if unknown)
development.here = false

# The title of environment (e.g. local or integration or production)
environment.title = Production

# Does it enable the Framework internal debug? (true only when emergency)
framework.debug = false

# one day: 86400000, three days: 259200000, five days: 432000000, one week: 604800000, one year: 31556926000

# Is development environment here? (used for various purpose, you should set false if unknown)
development.here = false

# The title of environment (e.g. local or integration or production)
environment.title = Production

# Does it enable the Framework internal debug? (true only when emergency)
framework.debug = false

# one day: 86400000, three days: 259200000, five days: 432000000, one week: 604800000, one year: 31556926000