} 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**:

        v.add(new DERTaggedObject(true, 2, new DERBitString(new byte[] { 0x02 })));
        // Tag 3 should be passed as ASN1TaggedObject which will be checked for APPLICATION tag class
        // Create a context-specific tagged object (wrong tag class)
        v.add(new DERTaggedObject(true, 3, new DERTaggedObject(true, 0, new DERSequence(new ASN1EncodableVector()))));

{* ../../docs_src/body_nested_models/tutorial005_py310.py hl[2,8] *}

The string will be checked to be a valid URL, and documented in JSON Schema / OpenAPI as such.

## Attributes with lists of submodels { #attributes-with-lists-of-submodels }

You can also use Pydantic models as subtypes of `list`, `set`, etc.:

	e.encoder = func() reedsolomon.Encoder {
		once.Do(func() {
			e, err := reedsolomon.New(dataBlocks, parityBlocks, reedsolomon.WithAutoGoroutines(int(e.ShardSize())))
			if err != nil {
				// Error conditions should be checked above.
				panic(err)
			}
			enc = e
		})
		return enc
	}
	return
}

// EncodeData encodes the given data and returns the erasure-coded data.
// It returns an error if the erasure coding failed.

      } 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) {

	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 {

      value > Int.MAX_VALUE -> Int.MAX_VALUE
      value < 0 -> 0
      else -> value.toInt()
    }
  } catch (_: NumberFormatException) {
    return defaultValue
  }
}

/** Closes this, ignoring any checked exceptions. */
fun Closeable.closeQuietly() {
  try {
    close()
  } catch (rethrown: RuntimeException) {
    throw rethrown
  } catch (_: Exception) {
  }
}

/**