<s>[INST] # This is a more advanced topic in machine learning, so bear with me.  I am trying to implement a capsule network (https://arxiv.org/abs/1710.09829) using PyTorch.  I wish to create a module that can learn to convert a vector into a matrix where the dimensions of the output matrix are based on the number of capsules in the previous layer and the number of capsules in the current layer.

# The conversion process would take a vector and learn the weights and bias for a affine transformation, i.e. matrix multiplication plus addition of a bias term.

# I am trying to create a module that can do this.  The first step is to initialize the weights and bias in the __init__ method. Here's what I have so far:

```python
import torch
import torch.nn as nn
import torch.nn.functional as F

class VectorToMatrix(nn.Module):
    def __init__(self, num_prev_capsules, num_cur_capsules):
        super(VectorToMatrix, self).__init__()
        self.num_prev_capsules = num_prev_capsules
        self.num_cur_capsules = num_cur_capsules
        self.weight = nn.Parameter(torch.Tensor(num_prev_capsules, num_cur_capsules))
        self.bias = nn.Parameter(torch.Tensor(1, 1, num_cur_capsules))
```

# My next step is to define the forward method.  I am thinking of doing a matrix multiplication and then adding the bias.  However, I am not sure how to structure the matrix multiplication as I want to repeat the vector for each of the `num_cur_capsules` and then do a matrix multiplication with `self.weight`.

# Can you help?[/INST]