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Solve the following python programming problem given the following code:
# Create trainer directory

! mkdir training_image/trainer

%%writefile training_image/trainer/train.py

# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""TFM common training driver."""

import json
import os

from absl import app
from absl import flags
from absl import logging
import gin
from official.common import distribute_utils
from official.common import flags as tfm_flags
from official.common import registry_imports
from official.core import task_factory
from official.core import train_lib
from official.core import train_utils
from official.modeling import performance

FLAGS = flags.FLAGS

def _get_model_dir(model_dir):
  """Defines utility functions for model saving.

  In a multi-worker scenario, the chief worker will save to the
  desired model directory, while the other workers will save the model to
  temporary directories. It’s important that these temporary directories
  are unique in order to prevent multiple workers from writing to the same
  location. Saving can contain collective ops, so all workers must save and
  not just the chief.
  """

  def _is_chief(task_type, task_id):
    return ((task_type == 'chief' and task_id == 0) or task_type is None)

  tf_config = os.getenv('TF_CONFIG')
  if tf_config:
    tf_config = json.loads(tf_config)

    if not _is_chief(tf_config['task']['type'], tf_config['task']['index']):
      model_dir = os.path.join(model_dir,
                               'worker-{}').format(tf_config['task']['index'])

 ...
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python code you requested.

Create training application code
Next, you create a training_image/trainer directory with a train.py script that contains the code for your training application.
The train.py training script is based on the common training driver from the TensorFlow NLP Modelling Toolkit. The common training driver script supports multiple NLP tasks (e.g., pre-training, GLUE and SQuAD fine-tuning) and multiple models (e.g., BERT, ALBERT). 
A set of configurations for a specific NLP task is called an experiment.  The toolkit includes a set of pre-defined experiments. When you invoke the script, you specificy an experiment type using the --experiment command line argument. There are two options for overriding the default experiment configuration:
- Specify one or multiple YAML configurations with updated settings using the --config_file command line argument
- Provide updated settings as a list of key/value pairs through the --params_override command line argument
If you specify both --config_file and --params_override, the settings in --params_override take precedence.
Retrieving the default experiment configuration and merging user provided settings is encapsulated in the official.core.train_utils.parse_configuration utility function from the TensorFlow Model Garden.
In the following cell, the common training driver script  has been adapted to work seamlessly on a distributed compute environment provisioned when running a Vertex Training job. The TensorFlow NLP Modelling Toolkit uses Orbit to implement a custom training loop. The custom training loop saves checkpoints, writes Tensorboard summaries, and saves a trained model to a storage location specified through the --model-dir command line argument. 
Note that when using the base common training driver in a distributed setting, each worker uses the same base storage location. To avoid conflicts when multiple workers write to the same storage location, the driver code has been modified so that each worker uses a different storage location based on its role in a Vertex...