# Create a media set batch pipeline with Code Repositories

This guide will walk you through a simple transformation example using the **Code Repositories** application. You will learn how to write and edit Python code with media sets.

## 1. Create a repository

Get started by creating a new repository. To do so, navigate to a [Project](../../getting-started/projects-and-resources/index.md) in Foundry, select **+ New** in the upper right, then select **Code repository**.

For this guide, we will write a Python transform. Give your repository a name and select **Python** in the dropdown menu under **Language template**. Then, select **Initialize repository**.

![New Python Transform Repository](../../../assets/39e2c121cd0a4769d2071d70429c0398a14e3005b90202a7dc1c060839314460.webp)

## 2. Import your data

If you already imported a raw media set to work with, you can move on to the next step. Otherwise, refer to the media sets guide on [how to import media into Foundry](../../data-integration/media-sets/index.md).

## 3. Import the `transforms-media` library

To use media set transformations, you must import the `transforms-media` library.

Navigate to the **Libraries** option from the left sidebar, then search for and select the `transforms-media` library. Then, choose to **Add and install library**.

![Transforms-media library](../../../assets/ff97e505c57bbc79dcf8c7d5267cf131d66a9a54c923478c7b77c9cd8c5a7aff.webp)

The status should show as **Library installed** once the install is complete.

![Transforms-media library installed](../../../assets/4c4b6ab485c3de46a09300c9af89429276e1b1e4836d6c7c6403f8d41546b1ce.webp)

## 4. Edit code

### Create a new file

Creating a Python transforms repository should auto-generate an `examples.py` file which you can use as your base. You can rename the file by selecting the ellipses icon, choosing **Rename**, and naming your file accordingly.
![Autogenerated Python file](../../../assets/c9ccfa7696c830e4c2e92d199b0a1814c319ab87444141bf10c959cd91a8ccc1.webp)

If you want to create a new file, select the ellipses icon and choose **New file** under the **Datasets** directory. Ensure you are creating a Python file for the purposes of this workflow.

### Import the correct libraries into your file

When working with media sets, you must use the `@transform` decorator. Media set inputs and outputs must be passed in using `transforms.mediasets.MediaSetInput` and `transforms.mediasets.MediaSetOutput` specifications:

```python
from transforms.api import transform
from transforms.mediasets import MediaSetInput, MediaSetOutput
@transform(
    images=MediaSetInput('/examples/images'),
    ...
)
```

### Ensure output file exists

If you are creating a media set output file, be sure to create it before referencing it in the transform decorator.

You can create this file by navigating to your Project folder (where you first created the Python transforms code repository), and selecting **New > Media set** from the dropdown menu.

![Create empty media set output](../../../assets/f241275d7f7e348c3b51572aa519f72f7ea35f4bb4de36f534db185460916c12.webp)

### Use `get_media_item()`, `get_media_item_by_path()`, or `list_media_items_by_path_with_media_reference()`

To transform each media item in your media set, you must reference these media items in your transformations with `get_media_item()` or `get_media_item_by_path()`.

Calls to `get_media_item()` and `get_media_item_by_path()` return a Python file-like stream object. All options accepted by [`io.open()` ↗](https://docs.python.org/3/library/io.html#io.open) are also supported. Items are read as streams, meaning that random access is not supported.

You can reference single media items as follows:

```python
from transforms.api import transform
from transforms.mediasets import MediaSetInput, MediaSetOutput
@transform(
    images=MediaSetInput('/examples/images'),
    output_images=MediaSetOutput('/examples/output_images')
)
def translate_images(images, output_images):
    image1 = images.get_media_item_by_path("image1") # using path
    image2 = images.get_media_item("ri.mio.main.media-item.123") # using RID
```

However, you will likely want to list all media items in your media set by putting them into a dataframe. You must list the media items with `list_media_items_by_path_with_media_reference()`:

```python
from transforms.api import transform, Output
from transforms.mediasets import MediaSetInput
@transform(
    images=MediaSetInput('/examples/images'),
    listing_output=Output('/examples/listed_images')
)
def translate_images(ctx, images, listing_output):
    media_items_listing = images.list_media_items_by_path_with_media_reference(ctx)

    # You can perform any PySpark function with the listed output
```

The listing will have the following schema:

```
+--------------------------+-----------+-------------------+
|        mediaItemRid      |    path   |  mediaReference  |
+--------------------------+-----------+-------------------+
| ri.mio.main.media-item.1 | item1.jpg |  {{reference1}}   |
| ri.mio.main.media-item.2 | item2.jpg |  {{reference2}}   |
| ri.mio.main.media-item.3 | item3.jpg |  {{reference3}}   |
+--------------------------+-----------+-------------------+
```

### Example: Turn a PDF media set into a new PNG media set

For this example, we will loop through all the media items in our PDF media set and save them as PNG images.

Input example:

![Input PDFs](../../../assets/916c7616cda3cbb63dfc96f13ea9bb3088c474251a571bbd25a0fac6cdf4fe9c.webp)

Notice that `list_media_items_by_path_with_media_reference()` returns a dataframe, which allows you to perform PySpark functions on the output. For this example, we are collecting the `mediaItemRid` column values to loop through as an array:

```python
from transforms.api import transform
from transforms.mediasets import MediaSetInput, MediaSetOutput


@transform(
    pdfs=MediaSetInput('INPUT_MEDIA_SET_PATH'),
    output_pngs=MediaSetOutput('OUTPUT_MEDIA_SET_PATH')
)
def upload_images(ctx, pdfs, output_images):
    media_items_listing = pdfs.list_media_items_by_path_with_media_reference(ctx)
    rid_list = media_items_listing.rdd.map(lambda x: x.mediaItemRid).collect()

    for rid in rid_list:
        output_png = pdfs.transform_document_to_png(rid, 0)
        output_images.put_media_item(output_png, "PNG_"+rid)
```

This will produce an output media set with PNG files (as noted from the metadata property):

![Output PNGs](../../../assets/b60a8a0eeeb92db48d1bc8febdfbf580a5eb59ee692950e60d746de79c1b1da9.webp)

Review a list of available out-of-the-box transformations in our [documentation](../../media-sets-advanced-formats/media-usage-limits/index.md#transformations).

## 4. Test your changes

After writing your data transformation code, you should test the changes you made. Testing ensures your code is working as expected before merging changes.

In Foundry, media sets can be branched similarly to code repositories. Branching is useful for testing the design of multi-step data pipelines. For instance, you can test changes to parts of data pipelines in isolation without breaking downstream dependencies for users who do not rely on your branch.

## 5. Commit your changes

Now, you can commit your changes in Code Repositories to label the work you performed. Before you make a commit, your work is auto-saved by default. A commit specifically labels your set of changes when you reach a stopping point.

:::callout{theme="success"}
Select the **Commit** button to commit your changes and run automatic checks on your code. Choosing the **Build** button also commits your changes, runs automatic code checks, and starts building your output dataset. To quickly test your changes without building your dataset to ensure your code passes the code checks, select **Commit**. Otherwise, you can skip ahead to [merge and build your changes](#6-merge-and-build-your-changes).
:::

## 6. Merge and build your changes

Once you are satisfied with the new media set output, work with any collaborators to merge, build, and view the changes.