how to use matrix multiplication to implement that covnert (Batch, a, b) tensor * (Batch, b) tensor into (Batch, a) tensor in tensorflow1.10

For example,

# Batch = 5, a = 25, b = 2
# tensor t1 shape: (Batch, a, b)
# tensor t2 shape: (Batch, b)
# tensor res shape: (Batch, a)
print(t1)
<tf.Tensor: id=466, shape=(2, 25, 2), dtype=int32, numpy=
array([[[ 1, 26],
        [ 2, 27],
        [ 3, 28],
        [ 4, 29],
        [ 5, 30],
        [ 6, 31],
        [ 7, 32],
        [ 8, 33],
        [ 9, 34],
        [10, 35],
        [11, 36],
        [12, 37],
        [13, 38],
        [14, 39],
        [15, 40],
        [16, 41],
        [17, 42],
        [18, 43],
        [19, 44],
        [20, 45],
        [21, 46],
        [22, 47],
        [23, 48],
        [24, 49],
        [25, 50]],

       [[ 1, 26],
        [ 2, 27],
        [ 3, 28],
        [ 4, 29],
        [ 5, 30],
        [ 6, 31],
        [ 7, 32],
        [ 8, 33],
        [ 9, 34],
        [10, 35],
        [11, 36],
        [12, 37],
        [13, 38],
        [14, 39],
        [15, 40],
        [16, 41],
        [17, 42],
        [18, 43],
        [19, 44],
        [20, 45],
        [21, 46],
        [22, 47],
        [23, 48],
        [24, 49],
        [25, 50]]], dtype=int32)>

print(t2)
<tf.Tensor: id=410, shape=(2, 2), dtype=int32, numpy=
array([[1, 0],
       [1, 0]], dtype=int32)>

# after matrix multiplication
print(res)
<tf.Tensor: id=474, shape=(2, 25), dtype=int32, numpy=
array([[ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
        17, 18, 19, 20, 21, 22, 23, 24, 25],
       [ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
        17, 18, 19, 20, 21, 22, 23, 24, 25]], dtype=int32)>

The way I think is to use matrix multiplication to only keep part like before, but it is hard for me to implement it.
If not mind could anyone help me?

from how to use matrix multiplication to implement that covnert (Batch, a, b) tensor * (Batch, b) tensor into (Batch, a) tensor in tensorflow1.10