\begin{split}\begin{align} \text{Minimize an objective:} & \\ & \sum_{i} a_i x_i + \sum_{i<j} b_{ij} x_i x_j + c, \\ \text{Subject to constraints:} & \\ & \sum_i a_i^{(c)} x_i + \sum_{i<j} b_{ij}^{(c)} x_i x_j+ c^{(c)} \le 0, \quad c=1, \dots, C_{\rm ineq.}, \\ & \sum_i a_i^{(d)} x_i + \sum_{i<j} b_{ij}^{(d)} x_i x_j + c^{(d)} = 0, \quad d=1, \dots, C_{\rm eq.}, \end{align}\end{split}
where $$\{ x_i\}_{i=1, \dots, N}$$ can be binary, integer, and real1 variables, $$a_{i}, b_{ij}, c$$ are real values and $$C_{\rm ineq.}, C_{\rm eq,}$$ are the number of inequality and equality constraints respectively.
The dimod.ConstrainedQuadraticModel class can contain this model and its methods provide convenient utilities for working with representations of a problem.