Shape mismatch when One-Hot-Encoding Train and Test data. Train_Data has more Dummy Columns than Test_data while using get_dummies with pipeline

I'm trying to create a get_dummies Class for my Data which I want to use it in Pipeline later:

class Dummies(BaseEstimator, TransformerMixin):
     def transform(self, df):
           dummies=pd.get_dummies(df[self.cat],drop_first=True) ## getting dummy cols
           df=pd.concat([df,dummies],axis=1) ## concatenating our dummies
           df.drop(self.cat,axis=1,inplace=True) ## dropping our original cat_cols

     def fit(self, df):
           self.cat=[]    
           for i in df.columns.tolist():    
               if i[0]=='c': ## My data has categorical cols start with 'c'  
                  self.cat.append(i)  ## Storing all my categorical_columns for dummies
              else:
                continue

Now when I call fit_transform on X_train and then transform X_test

z=Dummies()
X_train=z.fit_transform(X_train)
X_test=z.transform(X_test)

The columns in shape of X_train and X_test are Different:

X_train.shape
X_test.shape

Output:

(10983, 1797) (3661, 1529)

There are more Dummies in X_train than in my X_test. Clearly, my X_test has fewer categories than X_train. How do I write logic in my Class such that the categories in X_test broadcast to the shape of X_train? I want X_test to have the same number of dummy variables as my X_train.