There is a small typo in docs/guide/host_pc/setup_windows.md.

Should read `unfamiliar` rather than `unfarmilar`.

* Minor changes for 4.1
* Update conversion script to translate discontinuous data.
* In developer guide add disclaimer for version and correct intra-page links.

* * Update doc with donkey train command.
* Update doc with developer section for building own models in donkey 4.1
* Integrate changes from PR feedback

* Incorporate PR feedback
* Add empty records concept to tub
* Minor updates to conversion script

* Incorporate PR feedback
* Add empty record type into conversion script

* Add support for multi-dimensional model input and making the x, y interface symmetrical on the model / training interface: (#707)

- x/y_transform extract x, y as numpy arrays or floats out of the record
  - x/y_translate convert the numpy arrays of floats into tf-readable dictionaries used in tf data.
* Simplify model interface by implementing output_types() directly in the base class using output_shapes() dictionary.
* Adding developer guide for own model development
* Updated donkey command documentation
* Improve asserts and type hints in keras.py
* Added missing __init__.py in parts module.
* Add cool ascii text for donkey init and update yml and setup files including mypy
* Remove model training test from Travis and change the test to relative convergence. This avoids random fall overs in CI.
* Added test of tf.data as used in the training pipeline through re-implementation of data transformation from tub records to tf expected dictionaries, for all currently supported models.

* Improve pipeline use: move from building list of pipelines of single transforms to building a single pipeline with a list of transforms (actually just looping through function to go from TubRecord -> image -> augment -> normalise -> x and TubRecord -> y).

Fixed TfmIterators and TfmIterables.
* Iterables are the containers and are sized - these are the user objects
* Iterators are protocol objects to allow iteration, they have no logic and are local to the Iterables
* build/map_pipeline both return sized Iterables
* removed all batch logic, this is not required
* still commented but left code that uses generator based pipeline as this is simpler code

Using new small temporary pipeline generator
* this keeps the TubSequence lazy and avoids to roll out the pipeline into a list
* added a test to check consistency of the pipeline
* remove empty (after moved) augmentation file
* removed augmentation from old tub (as it's not needed and we removed the old augmentation)

New pipeline changes:
* moved augmentation into own class that is used above and can be used a  threaded or non-threaded part
* moved train functionality out of template and added 'donkey train', train.py just a simple dummy script for backward compatibility

* Address code reviews:
* Re-base on current dev to use un-altered sequence.py
* Add iterator consistency test to pipeline tests
* Undo changes in fast_stretch.py
* better tf shape manipulation
* small code improvements in training.py
* remove sleep in augment part

* Address code reviews:
* Add clearing of tubrecord list and minor renamings

change the public mqtt server name to one that's listening

The telemetry mqtt test is breaking w/ a timeout on the connection to the server

* Implement a Lazy transformable pipeline.
* Implement basic batching. However, this will need to be improved
  further for models with multiple outputs.
* Replace the old `Sequence` implementation used with a new `Pipeline`.

Test: Ran end to end tests.

* This makes reading a 100x faster.

Fix template docopt, as it's called managed.py and not drive.py

- Update cfg_basic.py with simulator parameters

Allow overriding WEB_CONTROL_PORT from env variable

USYD Brakes - Simulator

fix typo in drivemode return which checked pilot/throttle instead of brake

MakeMovie Utility Update - option for turning off line drawing

add in --user flag for MakeMovie so that you can select if you want to draw the line on the video or not.  default: true

Added more options for Installing Donkey Car on Windows.  

- Anaconda
- Native
- Windows Subsystem for Linux (WSL) - experiential

These options give more flexibility and could make deployment easier for some individuals.  I personally always install donkey car natively onto Python installed on the System.  I will be looking more into WSL going forward as it has some interesting benefits.

* Add __exit__ handlers again.
* Use `os.linesep` to deal with line separators consistently.

* Train still prompts you to move to then new entry point.
* Also fixes https://github.com/autorope/donkeycar/issues/655

* added example tub data as tar.gz in tests with 1000 records
* check validation data size in train
* created new test_train.py
* added support for read-only tub, required to create r/o tub in tmp dir
* changed loss in categorical model to equally weight throttle and steering
* changed mod on scripts to u+x

* Update setup.py

* Update setup.py

Made changes online.

Add configuration to allow creating sub directories for each tub (like the legacy tub) or by default store all recordings in the data directory directly. (#649)