}

// getImmediate checks that addr represents an immediate constant and returns its value.
func (p *Parser) getImmediate(prog *obj.Prog, op obj.As, addr *obj.Addr) int64 {
	if addr.Type != obj.TYPE_CONST || addr.Name != 0 || addr.Reg != 0 || addr.Index != 0 {
		p.errorf("%s: expected immediate constant; found %s", op, obj.Dconv(prog, addr))
	}
	return addr.Offset
}

</pre>


<h2 id="Blocks">Blocks</h2>

<p>
A <i>block</i> is a possibly empty sequence of declarations and statements
within matching brace brackets.
</p>

<pre class="ebnf">
Block = "{" StatementList "}" .
StatementList = { Statement ";" } .
</pre>

<p>
In addition to explicit blocks in the source code, there are implicit blocks:
</p>

<ol>

func (s *Site) Update(n Site) {
	siteLK.Lock()
	s.name = n.name
	s.region = n.region
	siteLK.Unlock()
}

// Name returns currently configured site name
func (s *Site) Name() string {
	siteLK.RLock()
	defer siteLK.RUnlock()

	return s.name
}

// Region returns currently configured site region
func (s *Site) Region() string {
	siteLK.RLock()
	defer siteLK.RUnlock()

	return s.region

In your editor, inside your function you will get type hints and completion everywhere (this wouldn't happen if you received a `dict` instead of a Pydantic model):

<img src="/img/tutorial/body/image03.png">

You also get error checks for incorrect type operations:

<img src="/img/tutorial/body/image04.png">

This is not by chance, the whole framework was built around that design.

}
h1.hidden {
    visibility:hidden;
    overflow:hidden;
    font-size:.9em;
}
.block {
    display:block;
    margin:3px 0 0 0;
}
.strong {
    font-weight:bold;
}

/*
 Fixes for a number of issues with the default stylesheet.
 */

/* Fixes huge font size in <pre>{@code} blocks. */
pre code {
    font-size:inherit;
}

/*

But there could be cases where you want to be able to set parameters on the dependency, without having to declare many different functions or classes.

Let's imagine that we want to have a dependency that checks if the query parameter `q` contains some fixed content.

But we want to be able to parameterize that fixed content.

## A "callable" instance

In Python there's a way to make an instance of a class a "callable".

// If the call is canceled through the context,
// the appropriate error will be returned.
type Stream struct {
	// responses from the remote server.
	// Channel will be closed after error or when remote closes.
	// All responses *must* be read by the caller until either an error is returned or the channel is closed.
	// Canceling the context will cause the context cancellation error to be returned.
	responses <-chan Response

    advance();
  }

  @Override
  public void close() throws IOException {
    if (in != null) {
      try {
        in.close();
      } finally {
        in = null;
      }
    }
  }

  /** Closes the current input stream and opens the next one, if any. */
  private void advance() throws IOException {
    close();
    if (it.hasNext()) {
      in = it.next().openStream();
    }
  }

  @Override

                                                     const TF_DataType* values,
                                                     int num_values);

// Checks the tensorflow::NodeDef built via the methods above to see if it can
// run on device_type.
TF_CAPI_EXPORT extern void TF_AttrBuilderCheckCanRunOnDevice(
    TF_AttrBuilder* builder, const char* device_type, TF_Status* status);

     * @return {@code true} if remote repositories should be re-checked for updated artifacts/metadata, {@code false}
     *         otherwise.
     */
    boolean isForceUpdate();

    /**
     * Enables/disabled forced checks for updated artifacts/metadata on remote repositories.
     *
     * @param forceUpdate {@code true} to forcibly check the remote repositories for updated artifacts/metadata, {@code