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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def from_numpy_matrix(cls, mat, variable_order=None, offset=0.0, interactions=None):
 """Create a binary quadratic model from a NumPy array. Args: mat (:class:`numpy.ndarray`):
 Coefficients of a quadratic unconstrained binary optimization (QUBO) model formatted as a square NumPy 2D array. variable_order (list, optional):
 If provided, labels the QUBO variables; otherwise, row/column indices are used. If `variable_order` is longer than the array, extra values are ignored. offset (optional, default=0.0):
 Constant offset for the binary quadratic model. interactions (iterable, optional, default=[]):
 Any additional 0.0-bias interactions to be added to the binary quadratic model. Returns: :class:`.BinaryQuadraticModel`: Binary quadratic model with vartype set to :class:`.Vartype.BINARY`. Examples: This example creates a binary quadratic model from a QUBO in NumPy format while adding an interaction with a new variable ('f'), ignoring an extra variable ('g'), and setting an offset. {'a': 1.0, 'b': 2.0, 'c': 3.0, 'd': 4.0, 'e': 5.0, 'f': 0.0} 10.0 0.0 2.5 """

        import numpy as np

        if mat.ndim != 2:
            raise ValueError("expected input mat to be a square 2D numpy array")

        num_row, num_col = mat.shape
        if num_col != num_row:
            raise ValueError("expected input mat to be a square 2D numpy array")

        if variable_order is None:
            variable_order = list(range(num_row))

        if interactions is None:
            interactions = []

        bqm = cls({}, {}, offset, Vartype.BINARY)

        for (row, col), bias in np.ndenumerate(mat):
            if row == col:
                bqm.add_variable(variable_order[row], bias)
            elif bias:
                bqm.add_interaction(variable_order[row], variable_order[col], bias)

        for u, v in interactions:
            bqm.add_interaction(u, v, 0.0)

        return bqm