## Resources

*   [TensorFlow.org](https://www.tensorflow.org)
*   [TensorFlow Tutorials](https://www.tensorflow.org/tutorials/)
*   [TensorFlow Official Models](https://github.com/tensorflow/models/tree/master/official)
*   [TensorFlow Examples](https://github.com/tensorflow/examples)
*   [TensorFlow Codelabs](https://codelabs.developers.google.com/?cat=TensorFlow)

    public static final String MAVEN_STYLE_PROJECT = MAVEN_STYLE_PREFIX + MAVEN_STYLE_PROJECT_NAME;

    /**
     * Build timestamp format.
     *
     * @since 3.0.0
     */
    @Config(source = Config.Source.MODEL, defaultValue = "yyyy-MM-dd'T'HH:mm:ssXXX")
    public static final String MAVEN_BUILD_TIMESTAMP_FORMAT = "maven.build.timestamp.format";

    /**
     * User controlled relocations.

///

/// tip

If you are wondering, "AlexNet", "ResNet", and "LeNet" are just names of Machine Learning <abbr title="Technically, Deep Learning model architectures">models</abbr>.

///

### Declare a *path parameter* { #declare-a-path-parameter }

Then create a *path parameter* with a type annotation using the enum class you created (`ModelName`):

          <description>Flags this Mojo to require online mode for its operation.</description>
          <defaultValue>false</defaultValue>
        </field>
        <field>
          <name>onlineRequired</name>
          <version>2.0.0+</version>
          <type>boolean</type>
          <description>Flags this Mojo to require online mode for its operation.</description>
          <defaultValue>false</defaultValue>

import org.apache.maven.api.annotations.Experimental;
import org.apache.maven.api.annotations.Immutable;
import org.apache.maven.api.annotations.Nonnull;
import org.apache.maven.api.annotations.Nullable;
import org.apache.maven.api.model.Dependency;

/**
 * A dependency's {@code Type} is uniquely identified by a {@code String},
 * and semantically represents a known <i>kind</i> of dependency.
 * <p>

```Python
from datetime import date

from pydantic import BaseModel

# Zadeklaruj parametr jako str
# i uzyskaj wsparcie edytora wewnątrz funkcji
def main(user_id: str):
    return user_id


# Model Pydantic
class User(BaseModel):
    id: int
    name: str
    joined: date
```

A one będą mogły zostać później użyte w następujący sposób:

```Python

`hashed_password`. To fix this, the declared `response_model` is cloned, if it is a Pydantic model class (or contains Pydantic model classes in it, e.g. in a `List[Item]`), the Pydantic model class(es) will be a different one (the "cloned" one). So, an object that is a subclass won't simply pass the validation and returned as-is, because it is no longer a sub-class of the cloned `response_model`. Instead, a new Pydantic model object will be created with the contents of the returned object. So, it will be a...

### Memory per Process { #memory-per-process }

Now, when the program loads things in memory, for example, a machine learning model in a variable, or the contents of a large file in a variable, all that **consumes a bit of the memory (RAM)** of the server.

  public static final String SEC_CH_UA_ARCH = "Sec-CH-UA-Arch";

  /**
   * The HTTP <a href="https://wicg.github.io/ua-client-hints/#sec-ch-ua-model">{@code
   * Sec-CH-UA-Model}</a> header field name.
   *
   * @since 30.0
   */
  public static final String SEC_CH_UA_MODEL = "Sec-CH-UA-Model";

  /**
   * The HTTP <a href="https://wicg.github.io/ua-client-hints/#sec-ch-ua-platform">{@code

/// tip | Tipp

Die Klasse `Config` wird nur für die Pydantic-Konfiguration verwendet. Weitere Informationen finden Sie unter <a href="https://docs.pydantic.dev/1.10/usage/model_config/" class="external-link" target="_blank">Pydantic Model Config</a>.

///

////

/// info