Is Tensorflow's Between-graph replication an example of data parallelism?

I have read distributed tensorflow documentation and this answer.
According to this, in data parallelism approach:

• The algorithm distributes the data between various cores.
• Each core independently tries to estimate the same parameter(s)
• Cores then exchange their estimate(s) with each other to come up with the right estimate for the step.

And in model parallelism approach:

• The algorithm sends the same data to all the cores.
• Each core is responsible for estimating different parameter(s)
• Cores then exchange their estimate(s) with each other to come up with the right estimate for all the parameters.

How do In-graph replication and Between-graph replication relate to these approaches?
This article says:

For example, different layers in a network may be trained in parallel on different GPUs. This training procedure is commonly known as "model parallelism" (or "in-graph replication" in the TensorFlow documentation).

And:

In "data parallelism" (or “between-graph replication” in the TensorFlow documentation), you use the same model for every device, but train the model in each device using different training samples.

Is that accurate?
From the Tensorflow DevSummit video linked in tensorflow documentation page: It looks like data is split and distributed to each worker. So isn't In-graph replication following data parallelism approach?


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