fix add broadcasted backward grad wrong values copy pointer data

test.py

@@ -1,92 +0,0 @@
-import norch
-
-W1 = norch.Tensor([[1], [2], [3], [4], [5], [6], [7], [8], [9], [10]], requires_grad=True)  # A has shape 10x1
-X = norch.Tensor([[1, 2, 3, 4, 5]])  # B has shape 1x5
-B1 = norch.Tensor([[1, 2, 3, 4, 5], [2, 1, 2, 3, 4], [3, 1,2,3, 4], [4, 1, 1, 1, 1,], [5,1,1,1,1], [6,1,1,1,1], [7,1,1,1,1], [8,1,1,1,1], [9,1,1,1,1], [10,1,1,1,1]], requires_grad=True)
-B1 = norch.Tensor([[1], [2], [3], [4], [5], [6], [7], [8], [9], [10]], requires_grad=True)  # A has shape 10x1
-
-W2 = norch.Tensor([[1], [2], [3], [4], [5], [6], [7], [8], [9], [10]], requires_grad=True).T
-B2 = norch.Tensor([[1, 2, 3, 4, 5]], requires_grad=True)
-
-
-Z1 = W1 @ X + B1
-
-# Perform matrix multiplication
-
-Z2 = W2 @ Z1 * B2
-print(Z2.shape)
-
-l = Z2.sum()
-l.backward()
-print(l)
-
-print("Resulting matrix shape:", B1.grad)
-
-
-"""import norch
-import norch.nn as nn
-import norch.optim as optim
-import random
-import math
-
-random.seed(1)
-
-class MyModel(nn.Module):
-    def __init__(self):
-        super(MyModel, self).__init__()
-        self.fc1 = nn.Linear(1, 10)
-        self.sigmoid = nn.Sigmoid()
-        self.fc2 = nn.Linear(10, 1)
-
-    def forward(self, x):
-        out = self.fc1(x)
-        out = self.sigmoid(out)
-        out = self.fc2(out)
-        
-        return out
-
-device = "cpu"
-epochs = 1
-
-model = MyModel().to(device)
-criterion = nn.MSELoss()
-optimizer = optim.SGD(model.parameters(), lr=0.001)
-loss_list = []
-
-x_values = [[0., 1], [0., 1], [0., 1], [0., 1], [0., 1], [0., 1], [0., 1], [0., 1], [0., 1], [0., 1]]
-
-y_true = []
-for x in x_values:
-    y_true.append([math.pow(math.sin(x[0]), 2), math.pow(math.sin(x[1]), 2)])
-
-batch_size = 5
-
-
-for epoch in range(epochs):
-    for x, target in zip(x_values, y_true):
-        x = norch.Tensor([x])
-        target = norch.Tensor([target])
-
-        x = x.to(device)
-        target = target.to(device)
-
-        outputs = model(x)
-
-        loss = criterion(outputs, target)
-        
-        optimizer.zero_grad()
-        loss.backward()
-        optimizer.step()
-
-        print('loss', loss, '\n\n')
-        print('weight', model.fc1.weight, '\n\n')
-        print('bias', model.fc1.bias, '\n\n')
-
-
-        if math.isnan(loss[0]):
-            print("Kkkkkkkkk")
-            exit()
-
-    #print(f'Epoch [{epoch + 1}/{epochs}], Loss: {loss[0]:.4f}')
-    loss_list.append(loss[0])
-"""