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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Flax seq2seq Example
<a href="https
Step1:  Imports
Step2:  Dataset
Step3:  Training
Step4:  Inference
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Python Code:
# Install CLU & Flax.
!pip install -q clu flax
example_directory = 'examples/seq2seq'
editor_relpaths = ('train.py', 'input_pipeline.py', 'models.py')
repo, branch = 'https://github.com/google/flax', 'main'
# (If you run this code in Jupyter[lab], then you're already in the
#  example directory and nothing needs to be done.)
#@markdown **Fetch newest Flax, copy example code**
#@markdown
#@markdown **If you select no** below, then the files will be stored on the
#@markdown *ephemeral* Colab VM. **After some time of inactivity, this VM will
#@markdown be restarted an any changes are lost**.
#@markdown
#@markdown **If you select yes** below, then you will be asked for your
#@markdown credentials to mount your personal Google Drive. In this case, all
#@markdown changes you make will be *persisted*, and even if you re-run the
#@markdown Colab later on, the files will still be the same (you can of course
#@markdown remove directories inside your Drive's `flax/` root if you want to
#@markdown manually revert these files).
if 'google.colab' in str(get_ipython()):
  import os
  os.chdir('/content')
  # Download Flax repo from Github.
  if not os.path.isdir('flaxrepo'):
    !git clone --depth=1 -b $branch $repo flaxrepo
  # Copy example files & change directory.
  mount_gdrive = 'no' #@param ['yes', 'no']
  if mount_gdrive == 'yes':
    DISCLAIMER = 'Note : Editing in your Google Drive, changes will persist.'
    from google.colab import drive
    drive.mount('/content/gdrive')
    example_root_path = f'/content/gdrive/My Drive/flax/{example_directory}'
  else:
    DISCLAIMER = 'WARNING : Editing in VM - changes lost after reboot!!'
    example_root_path = f'/content/{example_directory}'
    from IPython import display
    display.display(display.HTML(
        f'<h1 style="color:red;" class="blink">{DISCLAIMER}</h1>'))
  if not os.path.isdir(example_root_path):
    os.makedirs(example_root_path)
    !cp -r flaxrepo/$example_directory/* "$example_root_path"
  os.chdir(example_root_path)
  from google.colab import files
  for relpath in editor_relpaths:
    s = open(f'{example_root_path}/{relpath}').read()
    open(f'{example_root_path}/{relpath}', 'w').write(
        f'## {DISCLAIMER}\n' + '#' * (len(DISCLAIMER) + 3) + '\n\n' + s)
    files.view(f'{example_root_path}/{relpath}')
# Note : In Colab, above cell changed the working directory.
!pwd
Explanation: Flax seq2seq Example
<a href="https://colab.research.google.com/github/google/flax/blob/main/examples/seq2seq/seq2seq.ipynb" ><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Demonstration notebook for
https://github.com/google/flax/tree/main/examples/seq2seq
The Flax Notebook Workflow:
Run the entire notebook end-to-end and check out the outputs.
This will open Python files in the right-hand editor!
You'll be able to interactively explore metrics in TensorBoard.
Change some of the hyperparameters in the command-line flags in train.py for different hyperparameters. Check out the updated TensorBoard plots.
Update the code in train.py, models.py, and input_pipeline.py. 
   Thanks to %autoreload, any changes you make in the file will 
   automatically appear in the notebook. Some ideas to get you started:
Change the model.
Log some per-batch metrics during training.
Add new hyperparameters to models.py and use them in train.py.
Train on a different vocabulary by initializing CharacterTable with a
     different character set.
At any time, feel free to paste code from the source code into the notebook
   and modify it directly there!
Setup
End of explanation
from absl import app
app.parse_flags_with_usage(['seq2seq'])
from absl import logging
logging.set_verbosity(logging.INFO)
import jax
# Local imports from current directory - auto reload.
# Any changes you make to the three imported files will appear automatically.
%load_ext autoreload
%autoreload 2
import input_pipeline
import models
import train
Explanation: Imports
End of explanation
# Examples are generated on the fly.
ctable = input_pipeline.CharacterTable('0123456789+= ')
list(ctable.generate_examples(5))
batch = ctable.get_batch(5)
# A single query (/answer) is one-hot encoded.
batch['query'][0]
# Note how CTABLE encodes PAD=0, EOS=1, '0'=2, '1'=3, ...
ctable.decode_onehot(batch['query'][:1])
Explanation: Dataset
End of explanation
# Get a live update during training - use the "refresh" button!
# (In Jupyter[lab] start "tensorboard" in the local directory instead.)
if 'google.colab' in str(get_ipython()):
  %load_ext tensorboard
  %tensorboard --logdir=./workdirs
import time
workdir = f'./workdirs/{int(time.time())}'
# Train 2k steps & log 20 times.
app.parse_flags_with_usage([
    'seq2seq',
    '--num_train_steps=2000',
    '--decode_frequency=100',
])
state = train.train_and_evaluate(workdir=workdir)
if 'google.colab' in str(get_ipython()):
  #@markdown You can upload the training results directly to https://tensorboard.dev
  #@markdown
  #@markdown Note that everbody with the link will be able to see the data.
  upload_data = 'yes' #@param ['yes', 'no']
  if upload_data == 'yes':
    !tensorboard dev upload --one_shot --logdir ./workdirs --name 'Flax examples/seq2seq (Colab)'
Explanation: Training
End of explanation
inputs = ctable.encode_onehot(['2+40'])
# batch, max_length, vocab_size
inputs.shape
# Using different random seeds generates different samples.
preds = train.decode(state.params, inputs, jax.random.PRNGKey(0), ctable)
ctable.decode_onehot(preds)
Explanation: Inference
End of explanation
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