hypotheses (A list of tuples) – Hypothesis L*B*M = C to be tested where B is the parameters in regression Y = X*B. Each element is a tuple of length 2, 3, or 4:  (name, contrast_L)
 (name, contrast_L, transform_M)
 (name, contrast_L, transform_M, constant_C)
containing a string name , the contrast matrix L, the transform matrix M (for transforming dependent variables), and righthand side constant matrix constant_C, respectively. 
contrast_L : 2D array or an array of strings  Lefthand side contrast matrix for hypotheses testing. If 2D array, each row is an hypotheses and each column is an independent variable. At least 1 row (1 by k_exog, the number of independent variables) is required. If an array of strings, it will be passed to patsy.DesignInfo().linear_constraint.

transform_M : 2D array or an array of strings or None, optional  Left hand side transform matrix. If
None or left out, it is set to a k_endog by k_endog identity matrix (i.e. do not transform y matrix). If an array of strings, it will be passed to patsy.DesignInfo().linear_constraint. 
constant_C : 2D array or None, optional  Righthand side constant matrix. if
None or left out it is set to a matrix of zeros Must has the same number of rows as contrast_L and the same number of columns as transform_M If hypotheses is None: 1) the effect of each independent variable on the dependent variables will be tested. Or 2) if model is created using a formula, hypotheses will be created according to design_info . 1) and 2) is equivalent if no additional variables are created by the formula (e.g. dummy variables for categorical variables and interaction terms) 