Concat two tensors with different dimensions
I have two tensors a and b which are of different dimensions. a is of shape [100,100] and b is of the shape [100,3,10]. I want to concatenate these two tensors.
For example:
a = torch.randn(100,100)
tensor([[ 1.3236,  2.4250,  1.1547,  ..., -0.7024,  1.0758,  0.2841],
        [ 1.6699, -1.2751, -0.0120,  ..., -0.2290,  0.9522, -0.4066],
        [-0.3429, -0.5260, -0.7748,  ..., -0.5235, -1.8952,  1.2944],
        ...,
        [-1.3465,  1.2641,  1.6785,  ...,  0.5144,  1.7024, -1.0046],
        [-0.7652, -1.2940, -0.6964,  ...,  0.4661, -0.3998, -1.2428],
        [-0.4720, -1.0981, -2.3715,  ...,  1.6423,  0.0560,  1.0676]])

The tensor b is as follows:
tensor([[[ 0.4747, -1.9529, -0.0448,  ..., -0.9694,  0.8009, -0.0610],
         [ 0.5160,  0.0810,  0.1037,  ..., -1.7519, -0.3439,  1.2651],
         [-0.5975, -0.2000, -1.6451,  ...,  1.3082, -0.4023, -0.3105]],
        ...,

        [[ 0.4747, -1.9529, -0.0448,  ..., -0.9694,  0.8009, -0.0610],
         [ 0.1939,  1.0365, -0.0927,  ..., -2.4948, -0.2278, -0.2390],
         [-0.5975, -0.2000, -1.6451,  ...,  1.3082, -0.4023, -0.3105]]],
       dtype=torch.float64, grad_fn=&lt;CopyBackwards&gt;)

I want to concatenate such that the first row in tensor a of size [100] is concatenated with the first row in tensor b which is of size [3,10]. This should be applicable to all rows in both tensors. That is, in simple words, considering just the first row in a and b, I want to get an output with size [100,130] as follows:
[ 1.3236,  2.4250,  1.1547,  ..., -0.7024,  1.0758,  0.2841, 0.4747, -1.9529, -0.0448,  ..., -0.9694,  0.8009, -0.0610, 0.5160,  0.0810,  0.1037,  ..., -1.7519, -0.3439,  1.2651, -0.5975, -0.2000, -1.6451,  ...,  1.3082, -0.4023, -0.3105]

In order to do this, I performed unsqueezed to tensor a to get the two tensors in the same dimensions as follows.
a = a.unsqueeze(1)

When I perform torch.cat([a,b], I still get an error. Can somebody help me in solving this?
Thanks in advance.

Reshape b tensor accordingly and then merge it to a using torch.cat on 1 dim
torch.cat((a, b.reshape(100, -1)), dim=1)

