Integrated gradients is an attribution method which works by approximating the following integral, where $F : \mathbb{R}^n \rightarrow \mathbb{R}$ is the network, $x$ is the input, $x'$ is the baseline, and $i$ is the feature.
$$
\mathrm{IntegratedGradients}i(x) = (x_i - x_i') \times \int{\alpha=0}^1 \frac{\partial F(x' + \alpha (x - x'))}{\partial x_i} d\alpha
$$
Intuitively we're accumulating all the change in output from changing $x'$ to $x$ along the linear path $\alpha x' + (1 - \alpha) x$. For a concrete example in words, let $x_i$ be the weight of a heavy exotic frog, $x_i'$ be the average weight over all frogs, $F(x)$ be the estimated jump height of a frog with all the features $x$ (one of which is weight). Given this, the right hand side of the equation above can be interpreted as
$$
\begin{aligned}