}

            // Verify that calculateMIC was never called
            verify(mockCtx, never()).calculateMIC(any());
            // Verify that supportsIntegrity was checked once
            verify(mockCtx, times(1)).supportsIntegrity();
            // Due to short-circuit evaluation, isEstablished should never be called
            // when supportsIntegrity returns false

      if (node.previousSibling == null) {
        // requireNonNull is safe because we checked that not *both* siblings were null.
        keyList.head = requireNonNull(node.nextSibling);
      } else {
        node.previousSibling.nextSibling = node.nextSibling;
      }

      if (node.nextSibling == null) {
        // requireNonNull is safe because we checked that not *both* siblings were null.

      } catch (Exception | Error unsafeFailure) { // sneaky checked exception
        thrownUnsafeFailure = unsafeFailure;
        // Catch absolutely everything and fall through to AtomicReferenceFieldUpdaterAtomicHelper.
        try {
          helper = new AtomicReferenceFieldUpdaterAtomicHelper();
        } catch (Exception // sneaky checked exception
            | Error atomicReferenceFieldUpdaterFailure) {

{* ../../docs_src/security/tutorial003_an_py310.py hl[3,79:81] *}

### Check the password { #check-the-password }

At this point we have the user data from our database, but we haven't checked the password.

Let's put that data in the Pydantic `UserInDB` model first.

You should never save plaintext passwords, so, we'll use the (fake) password hashing system.

	pp.SetContentType("image/jpeg")
	pp.SetUserMetadata("uuid", "14365123651274")
	pp.SetKeyStartsWith("user/user1/filename")
	pp.SetContentLengthRange(100, 999999) // not testable from this layer, condition is checked in the API handler.
	pp.SetSuccessStatusAction("201")
	pp.SetCondition("eq", "X-Amz-Credential", "KVGKMDUQ23TCZXTLTHLP/20160727/us-east-1/s3/aws4_request")
	pp.SetCondition("eq", "X-Amz-Algorithm", "AWS4-HMAC-SHA256")

* Validate **complex structures**:
    * Use of hierarchical Pydantic models, Python `typing`’s `List` and `Dict`, etc.
    * And validators allow complex data schemas to be clearly and easily defined, checked and documented as JSON Schema.
    * You can have deeply **nested JSON** objects and have them all validated and annotated.
* **Extensible**:

      } catch (Exception | Error unsafeFailure) { // sneaky checked exception
        thrownUnsafeFailure = unsafeFailure;
        // Catch absolutely everything and fall through to AtomicReferenceFieldUpdaterAtomicHelper.
        try {
          helper = new AtomicReferenceFieldUpdaterAtomicHelper();
        } catch (Exception // sneaky checked exception
            | Error atomicReferenceFieldUpdaterFailure) {

   * true when removing a connection from the pool; otherwise a racing caller might get it from the
   * pool when it shouldn't. Symmetrically, this must always be checked before returning a
   * connection from the pool.
   *
   * Once true this is always true. Guarded by this.
   */
  var noNewExchanges = false

  /**

	encrypt.SseCustomerKey:       true,
	encrypt.SseCustomerKeyMD5:    true,
}

// checkPostPolicy - apply policy conditions and validate input values.
// Note that content-length-range is checked in the API handler function PostPolicyBucketHandler.
// formValues is the already-canonicalized form values from the POST request.
func checkPostPolicy(formValues http.Header, postPolicyForm PostPolicyForm) error {

  }

  @SuppressWarnings("CatchingUnchecked") // sneaky checked exception
  protected final boolean supportsValuesHashCode(Map<K, V> map) {
    // get the first non-null value
    Collection<V> values = map.values();
    for (V value : values) {
      if (value != null) {
        try {
          int unused = value.hashCode();
        } catch (Exception e) { // sneaky checked exception
          return false;
        }