Ranking (dimensions) of a tensor

The rank of a tensor is the number of dimensions it has, that is, the number of indices that are required to specify any particular element of that tensor.

The rank of a tensor can be ascertained with this, for example:

tf.rank(t2)

The output will be as follows:

<tf.Tensor: id=53, shape=(), dtype=int32, numpy=3>
(the shape is () because the output here is a scalar value)