///

## Create a Project { #create-a-project }

First, create a directory for your project.

What I normally do is that I create a directory named `code` inside my home/user directory.

And inside of that I create one directory per project.

<div class="termy">

```console
// Go to the home directory
$ cd
// Create a directory for all your code projects
$ mkdir code

| xlMetaV2       | msgp bin array | Bin array with serialized metadata
| crc            | msgp uint      | Lower 32 bits of 64 bit xxhash of previous array contents (v1.2+ only)
| inline data    | binary         | Inline data if any, see Inline Data section for encoding.  
| [EOF] | |

## v1.0-v1.2 Versions

`xl.meta` carries three types of object entries which designate the type of version object stored.

- ObjectType (default)

# for verifying changes to the TFCI scripts system, and most users won't need
# to interact with it at all.
source "${BASH_SOURCE%/*}/utilities/setup.sh"

echo "==TFCI== env outside of tfrun:"
env
echo "==TFCI== env inside of tfrun:"

          <description>Flags this Mojo to require running inside of a project.</description>
          <defaultValue>true</defaultValue>
        </field>
        <field>
          <name>projectRequired</name>
          <version>2.0.0+</version>
          <type>boolean</type>
          <description>Flags this Mojo to require running inside of a project.</description>
          <defaultValue>true</defaultValue>

Since models are practically programs that TensorFlow executes, using untrusted
models or graphs is equivalent to running untrusted code.

If you need to run untrusted models, execute them inside a
[**sandbox**](https://developers.google.com/code-sandboxing). Memory corruptions
in TensorFlow ops can be recognized as security issues only if they are
reachable and exploitable through production-grade, benign models.

## Example { #example }

Here's an example *path operation* with parameters using some of the above types.

{* ../../docs_src/extra_data_types/tutorial001_an_py310.py hl[1,3,12:16] *}

Note that the parameters inside the function have their natural data type, and you can, for example, perform normal date manipulations, like:

	if !globalBrowserEnabled {
		globalBrowserEnabled = true
		defer func() { globalBrowserEnabled = false }()
	}

	// initialize HTTP NewRecorder, this records any mutations to response writer inside the handler.
	rec := httptest.NewRecorder()
	req, perr := newPostRequestV4("", bucketName, objectName, []byte("pwned"), credentials.AccessKey, credentials.SecretKey)
	if perr != nil {

But there are specific cases where it's useful to get the `Request` object.

## Use the `Request` object directly { #use-the-request-object-directly }

Let's imagine you want to get the client's IP address/host inside of your *path operation function*.

For that you need to access the request directly.

{* ../../docs_src/using_request_directly/tutorial001.py hl[1,7:8] *}

    /**
     * Gets the file size.
     *
     * @return the file size in bytes
     */
    long length();

    /**
     * Gets the file index.
     *
     * @return the file index inside the parent directory
     */
    int getFileIndex();

  <expressions>
    <expression>
      <syntax>localRepository</syntax>
      <configuration>
        <![CDATA[
      Inside ~/.m2/settings.xml:

      <localRepository>/path/to/local/repository</localRepository>
      ]]></configuration>
      <description>The ArtifactRepository instance referencing the local artifact