Pytorch 1D tensors expected but got 2D tensors

I have been working on making neural network from scratch in python. The input tensor is of shape [400,3] and target_tensor has the shape [400]. I am getting error during taking derivative of weights. Below are functions:

def sigmoid(z):
    return 1 / (1 + torch.exp(-z))

def nueral_net(data,weights,bias):
    return sigmoid( ( data @ weights ) + bias )

def loss_function(prediction,actual,m):
    return (-1/m) * (torch.sum(actual * torch.log(prediction) + (1-actual) 
    * torch.log(1- prediction)))

w = torch.randn(input_tensor.shape[1],1)
b = torch.randn(1,1)

predictions = nueral_net(input_tensor.float() , w, b) #Applying model
loss = loss_function(predictions,target_tensor.unsqueeze(1),400)
dw = (1/400) * torch.dot(input_tensor,(predictions - target_tensor).T)

Running this throws an error:

RuntimeError                              Traceback (most recent call last)
<ipython-input-26-632338d8fd16> in <module>
      1 predictions = nueral_net(input_tensor.float() , w, b) #Applying model
      2 loss = loss_function(predictions,target_tensor.unsqueeze(1),400)
----> 3 dw = (1/400) * torch.dot(input_tensor,(predictions - target_tensor).T)
      4 db = (1/400) * torch.sum(predictions - target_tensor)
      5 #m = input_tensor.shape[0]
    
RuntimeError: 1D tensors expected, but got 2D and 2D tensor

If we see the doc of torch.dot :
torch.dot(input, other, *, out=None) → Tensor Computes the dot product of two 1D tensors.

NOTE : Unlike NumPy’s dot, torch.dot intentionally only supports computing the dot product of two 1D tensors with the same number of elements.

Parameters
input (Tensor) – first tensor in the dot product, must be 1D.
other (Tensor) – second tensor in the dot product, must be 1D.

Coming to your question....both input_tensor & (predictions - target_tensor).T in 2D.
Please make it 1D