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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 get_identifiability_dataframe(self,singular_value=None,precondition=False):
 """get the parameter identifiability as a pandas dataframe Parameters singular_value : int the singular spectrum truncation point. Defaults to minimum of non-zero-weighted observations and adjustable parameters precondition : bool flag to use the preconditioned hessian (xtqt + sigma_theta^-1). Default is False Returns ------- pandas.DataFrame : pandas.DataFrame A pandas dataframe of the V_1**2 Matrix with the identifiability in the column labeled "ident" """

        if singular_value is None:
            singular_value = int(min(self.pst.nnz_obs, self.pst.npar_adj))
        #v1_df = self.qhalfx.v[:, :singular_value].to_dataframe() ** 2
        xtqx = self.xtqx
        if precondition:
            xtqx = xtqx + self.parcov.inv
        #v1_df = self.xtqx.v[:, :singular_value].to_dataframe() ** 2
        v1_df = xtqx.v[:, :singular_value].to_dataframe() ** 2
        v1_df["ident"] = v1_df.sum(axis=1)
        return v1_df