Solving error for mismatch tensor size
code:num_epochs = 100loss_history =for i in range(num_epochs):model.train()total_loss =0.0for batch in train_dataloader:pv, fv, ptf, ftf = batchfv = fv.unsqueeze(-1)optimiser.zero_grad()pom = torch.ones_like(pv)ftf = ftf.view(ptf.shape[0], fv.shape[1], ptf.shape[2])outputs = model(past_values=pv,past_time_features=ptf,past_observed_mask=pom,future_values=fv,future_time_features=ftf)loss = outputs.lossloss.backward()optimiser.step()total_loss +=loss.item()avg_loss = total_loss/len(train_dataloader)loss_history.append(avg_loss)RuntimeError                              Traceback (most recent call last)in <cell line: 3>()10     pom = torch.ones_like(pv)11     ftf = ftf.view(ptf.shape[0], fv.shape[1], ptf.shape[2])—> 12     outputs = model(past_values=pv,past_time_features=ptf,past_observed_mask=pom,future_values=fv,future_time_features=ftf)13     loss = outputs.loss14     loss.backward()14 frames/usr/local/lib/python3.10/dist-packages/torch/nn/modules/linear.py in forward(self, input)114115     def forward(self, input: Tensor) → Tensor:→ 116         return F.linear(input, self.weight, self.bias)117118     def extra_repr(self) → str:RuntimeError: mat1 and mat2 shapes cannot be multiplied (3072x46 and 34x64)