// TF_DeleteSession.
  // TF_Graph may only / must be deleted when
  //   sessions.size() == 0 && delete_requested == true
  //
  // TODO(b/74949947): mutations currently trigger a warning instead of a bad
  // status, this should be reverted when possible.
  tensorflow::gtl::FlatMap<TF_Session*, tensorflow::string> sessions
      TF_GUARDED_BY(mu);

 * will return {@code true} for many domains which (currently) are not hosts, such as {@code "com"},
 * but given that any public suffix may become a host without warning, it is better to err on the
 * side of permissiveness and thus avoid spurious rejection of valid sites. Of course, to actually
 * determine addressability of any host, clients of this class will need to perform their own DNS

    private SetView() {} // no subclasses but our own

    /**
     * Returns an immutable copy of the current contents of this set view. Does not support null
     * elements.
     *
     * <p><b>Warning:</b> this may have unexpected results if a backing set of this view uses a
     * nonstandard notion of equivalence, for example if it is a {@link TreeSet} using a comparator

The same example from above, returning an `HTMLResponse`, could look like:

```Python hl_lines="2  7  19"
{!../../docs_src/custom_response/tutorial003.py!}
```

/// warning

A `Response` returned directly by your *path operation function* won't be documented in OpenAPI (for example, the `Content-Type` won't be documented) and won't be visible in the automatic interactive docs.

///

/// info

			jwtgo.ValidationErrorClaimsInvalid)
	}

	// Signature is valid, lets validate the claims for
	// other fields such as expiry etc.
	return claims.Valid()
}

// ParseUnverifiedStandardClaims - WARNING: Don't use this method unless you know what you're doing
//
// This method parses the token but doesn't validate the signature. It's only
// ever useful in cases where you know the signature is valid (because it has

## Use Case

Let's start with an example **use case** and then see how to solve it with this.

Let's imagine that you have some **machine learning models** that you want to use to handle requests. 🤖

            assertThat("Has errors", richReport.errors.map { it.message }, CoreMatchers.equalTo(errors.toList()))
        }

        fun assertHasWarnings(vararg warnings: String) {
            assertThat("Has warnings", richReport.warnings.map { it.message }, CoreMatchers.equalTo(warnings.toList()))
        }

        fun assertHasInformation(vararg information: String) {

Lets start the lamdba handler.

```
python lambda_handler.py
 * Serving Flask app 'webhook'
 * Debug mode: off
WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.
 * Running on http://127.0.0.1:5000
Press CTRL+C to quit
```

## Start MinIO with Lambda target

            return false;
        }
        try {
            return isJavaVersionCompatible(jdk, version);
        } catch (NumberFormatException e) {
            problems.add(new ModelProblemCollectorRequest(Severity.WARNING, Version.BASE)
                    .setMessage("Failed to determine JDK activation for profile " + profile.getId()
                            + " due invalid JDK version: '" + version + "'")

     * {@link Session#resolveDependencies resolveDependencies(...)} when at least one dependency
     * cannot be associated to any type specified in the {@code desiredTypes} argument.
     * Plugins can choose to report a warning to users when unresolved paths exist.
     */
    PathType UNRESOLVED = new PathType() {
        @Override
        public String name() {
            return "UNRESOLVED";
        }

        @Override