Which is more popular/common way of representing a gradient in AI community: as a row or column vector?

Consider the following remark about writing gradients from the chapter named Vector Calculus from the test book titled Mathematics for Machine Learning by Marc Peter Deisenroth et al.

Remark (Gradient as a Row Vector). It is not uncommon in the literature to define the gradient vector as a column vector, following the convention that vectors are generally column vectors. The reason why we define the gradient vector as a row vector is twofold: First, we can consistently generalize the gradient to vector-valued functions $$f: \mathbb{R}^n \rightarrow \mathbb{R}^m$$ (then the gradient becomes a matrix). Second, we can immediately apply the multi-variate chain rule without paying attention to the dimension of the gradient.

The above remark implicitly says that there is no standard in writing the gradients. So, I can write a gradient of a scalar-valued multivariate function either as a column vector or as a row vector.

But, I want to know which is more common in the AI community?

A more relevant issue is whether one uses the so-called "numerator layout" or "denominator layout". In the numerator layout, $$\partial f / \partial x$$ is $$\mathbb{R}^{n\times m}$$, where $$f \in \mathbb{R}^n$$, $$x \in \mathbb{R}^m$$. The denominator layout is the other way ($$\partial f / \partial x$$ is $$\mathbb{R}^{m \times n}$$).