Multiply a [3, 2, 3] by a [3, 2] tensor in pytorch (dot product along dimension)
Given the following tensors x and y with shapes  [3,2,3] and [3,2]. I want to multiply the tensors along the 2nd dimension, this is expected to be a kind of dot product and scaling along the axis and return a [3,2,3] tensor.
import torch
a  = [[[0.2,0.3,0.5],[-0.5,0.02,1.0]],[[0.01,0.13,0.06],[0.35,0.12,0.0]], [[1.0,-0.3,1.0],[1.0,0.02, 0.03]] ]
b = [[1,2],[1,3],[0,2]]
x = torch.FloatTensor(a) # shape [3,2,3]
y = torch.FloatTensor(b) # shape [3,2]

The expected output :
Expected output shape should be [3,2,3]
#output = [[[0.2,0.3,0.5],[-1.0,0.04,2.0]],[[0.01,0.13,0.06],[1.05,0.36,0.0]], [[0.0,0.0,0.0],[2.0,0.04, 0.06]] ]

I have tried the two below but none of them is giving the desired output and output shape.
torch.matmul(x,y)
torch.matmul(x,y.unsqueeze(1).shape)

What is the best way to fix this?

This is just broadcasted multiply. So you can insert a unitary dimension on the end of y to make it a [3,2,1] tensor and then multiply by x. There are multiple ways to insert unitary dimensions.
# all equivalent
x * y.unsqueeze(2)
x * y[..., None]
x * y[:, :, None]
x * y.reshape(3, 2, 1)

You could also use torch.einsum.
torch.einsum('abc,ab-&gt;abc', x, y)

