type Cell struct {
	Value      string
	Attributes []color.Attribute
}

type Row struct {
	Cells []Cell
}

func NewCell(value string, attributes ...color.Attribute) Cell {
	attrs := append([]color.Attribute{}, attributes...)
	return Cell{value, attrs}
}

func (cell Cell) String() string {
	if len(cell.Attributes) == 0 {
		return cell.Value
	}
	s := color.New(cell.Attributes...)
	s.EnableColor()

/// warning

Notice that `Field` is imported directly from `pydantic`, not from `fastapi` as are all the rest (`Query`, `Path`, `Body`, etc).

///

## Declare model attributes

You can then use `Field` with model attributes:

{* ../../docs_src/body_fields/tutorial001_an_py310.py hl[11:14] *}

`Field` works the same way as `Query`, `Path` and `Body`, it has all the same parameters, etc.

private
val zipEntryBaseTimestamp = LocalDateTime.of(1980, 2, 1, 0, 0, 0)
    .atZone(ZoneId.systemDefault()).toInstant().toEpochMilli()


object Attributes {
    val artifactType = Attribute.of("artifactType", String::class.java)
    val minified = Attribute.of("minified", Boolean::class.javaObjectType)
}


class JarAnalyzer(
    private val shadowPackage: String,
    private val keepPackages: Set<String>,

## Create your data model

Then you declare your data model as a class that inherits from `BaseModel`.

Use standard Python types for all the attributes:

{* ../../docs_src/body/tutorial001_py310.py hl[5:9] *}


The same as when declaring query parameters, when a model attribute has a default value, it is not required. Otherwise, it is required. Use `None` to make it just optional.

                }
                final NamedNodeMap attributes = imgNode.getAttributes();
                final String thumbnailUrl = getThumbnailSrc(responseData.getUrl(), attributes);
                final Integer height = getAttributeAsInteger(attributes, "height");
                final Integer width = getAttributeAsInteger(attributes, "width");
                if (!fessConfig.isThumbnailHtmlImageUrl(thumbnailUrl)) {

'right' ? -1 : 1;\n    styles[sideA] = top * invertTop;\n    styles[sideB] = left * invertLeft;\n    styles.willChange = `${sideA}, ${sideB}`;\n  }\n\n  // Attributes\n  const attributes = {\n    'x-placement': data.placement,\n  };\n\n  // Update `data` attributes, styles and arrowStyles\n  data.attributes = { ...attributes, ...data.attributes };\n  data.styles = { ...styles, ...data.styles };\n  data.arrowStyles = { ...data.offsets.arrow, ...data.arrowStyles };\n\n  return data;\n}\n","import getOppositePlacement...

The same way, this database wouldn't receive a Pydantic model (an object with attributes), only a `dict`.

You can use `jsonable_encoder` for that.

It receives an object, like a Pydantic model, and returns a JSON compatible version:

{* ../../docs_src/encoder/tutorial001_py310.py hl[4,21] *}

        </field>
        <field>
          <name>os</name>
          <version>1.0.0</version>
          <description><![CDATA[
            Specifies that this profile will be activated when matching OS attributes are detected.
          ]]></description>
          <association>
            <type>ActivationOS</type>
          </association>
        </field>
        <field>
          <name>property</name>

 * <pre>
 *   &#64;Mojo( name = "&lt;goal-name&gt;" )
 * </pre>
 * <p>
 * There are also a number of attributes which can be used to control how and when the
 * <code>Mojo</code> is executed:
 * </p>
 * <table border="1">
 *  <caption>mojo annotation attributes</caption>
 *  <tr>
 *      <th>Descriptor Element</th>
 *      <th>Annotation</th>
 *      <th>Required?</th>
 *      <th>Notes</th>

<artifactId>project-artifact</artifactId>
<version>0.0.0.0</version>
<packaging>type</packaging>
    ]]></configuration>
    <description> This is the Artifact instance created from the essential project
      attributes: groupId, artifactId, version, and packaging (with a default packaging of
      'jar').
    </description>
  </expression>
  <expression>
    <syntax>project.parent</syntax>
    <configuration>
      <![CDATA[