Clarifications on training job parameters with Tensorflow - python-3.x

Question

Im using the new Tensorflow object detection API.
I need to replicate training parameters used on a paper but Im a bit confused.
In the paper is stated
When training neural network models, their base confguration is similar to that used to
train on the COCO 2017 dataset. For the unambiguous comparison of the selected models, the total number of
training steps was set to 100 equal to 100′000 iterations of learning.
Inside model_main_tf2.py, which is the script used to start the training, I can read the following:
"""Creates and runs TF2 object detection models.
For local training/evaluation run:
PIPELINE_CONFIG_PATH=path/to/pipeline.config
MODEL_DIR=/tmp/model_outputs
NUM_TRAIN_STEPS=10000
SAMPLE_1_OF_N_EVAL_EXAMPLES=1
python model_main_tf2.py -- \
--model_dir=$MODEL_DIR --num_train_steps=$NUM_TRAIN_STEPS \
--sample_1_of_n_eval_examples=$SAMPLE_1_OF_N_EVAL_EXAMPLES \
--pipeline_config_path=$PIPELINE_CONFIG_PATH \
--alsologtostderr
"""
Also, you can specify the num_steps and total_steps parameters in the pipeline.config file (used by the training script):
train_config: {
batch_size: 1
sync_replicas: true
startup_delay_steps: 0
replicas_to_aggregate: 8
num_steps: 50000
optimizer {
momentum_optimizer: {
learning_rate: {
cosine_decay_learning_rate {
learning_rate_base: .16
total_steps: 50000
warmup_learning_rate: 0
warmup_steps: 2500
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
So, what Im not understanding is how should I map what is written in the paper with tensorflow parameters.
What is the num steps and total_steps inside the pipeline.config file?
What is the NUM_TRAIN_STEPS argument instead?
Does it overwrite config file steps or its a completely different thing?
If more details are needed feel free to ask.