with `import torch    import torch.nn as nn    import torch.optim as optim        # Define the neural network    class SingleNeuron(nn.Module):        def __init__(self):            super(SingleNeuron, self).__init__()            self.linear = nn.Linear(1, 1)                    def forward(self, x):            return self.linear(x)        # Create the synthetic data    x_train = torch.tensor([[1.0], [2.0], [3.0], [4.0]], dtype=torch.float32)    y_train = torch.tensor([[2.0], [4.0], [6.0], [8.0]], dtype=torch.float32)        # Create the neural network    model = SingleNeuron()        # Define the loss function and optimizer    criterion = nn.MSELoss()    optimizer = optim.SGD(model.parameters(), lr=0.01)        # Train the neural network    for epoch in range(1, 1001):        # Forward pass        y_pred = model(x_train)                # Compute loss        loss = criterion(y_pred, y_train)                # Backward pass and optimization        optimizer.zero_grad()        loss.backward()        optimizer.step()                # Print the loss every 100 epochs        if epoch % 100 == 0:            print(f"Epoch {epoch}: Loss = {loss.item()}")        # Make a prediction for x = 5    x_test = torch.tensor([[5.0]], dtype=torch.float32)    y_pred = model(x_test)    y_pred.item()`            Epoch 100: Loss = 0.03825576975941658    Epoch 200: Loss = 0.02100197970867157    Epoch 300: Loss = 0.01152981910854578    Epoch 400: Loss = 0.006329738534986973    Epoch 500: Loss = 0.0034749575424939394    Epoch 600: Loss = 0.0019077073084190488    Epoch 700: Loss = 0.001047312980517745    Epoch 800: Loss = 0.0005749554838985205    Epoch 900: Loss = 0.0003156439634039998    Epoch 1000: Loss = 0.00017328384274151176        Invoking: `Python_REPL` with `x_test.item()`            The prediction for x = 5 is 10.000173568725586.        > Finished chain.    'The prediction for x = 5 is 10.000173568725586.'PreviousPowerBI DatasetNextSpark DataframeUsing ZERO_SHOT_REACT_DESCRIPTIONUsing
This notebook showcases an agent designed to write and execute Python code to answer a question.
This notebook showcases an agent designed to write and execute Python code to answer a question. ->: with `import torch    import torch.nn as nn    import torch.optim as optim        # Define the neural network    class SingleNeuron(nn.Module):        def __init__(self):            super(SingleNeuron, self).__init__()            self.linear = nn.Linear(1, 1)                    def forward(self, x):            return self.linear(x)        # Create the synthetic data    x_train = torch.tensor([[1.0], [2.0], [3.0], [4.0]], dtype=torch.float32)    y_train = torch.tensor([[2.0], [4.0], [6.0], [8.0]], dtype=torch.float32)        # Create the neural network    model = SingleNeuron()        # Define the loss function and optimizer    criterion = nn.MSELoss()    optimizer = optim.SGD(model.parameters(), lr=0.01)        # Train the neural network    for epoch in range(1, 1001):        # Forward pass        y_pred = model(x_train)                # Compute loss        loss = criterion(y_pred, y_train)                # Backward pass and optimization        optimizer.zero_grad()        loss.backward()        optimizer.step()                # Print the loss every 100 epochs        if epoch % 100 == 0:            print(f"Epoch {epoch}: Loss = {loss.item()}")        # Make a prediction for x = 5    x_test = torch.tensor([[5.0]], dtype=torch.float32)    y_pred = model(x_test)    y_pred.item()`            Epoch 100: Loss = 0.03825576975941658    Epoch 200: Loss = 0.02100197970867157    Epoch 300: Loss = 0.01152981910854578    Epoch 400: Loss = 0.006329738534986973    Epoch 500: Loss = 0.0034749575424939394    Epoch 600: Loss = 0.0019077073084190488    Epoch 700: Loss = 0.001047312980517745    Epoch 800: Loss = 0.0005749554838985205    Epoch 900: Loss = 0.0003156439634039998    Epoch 1000: Loss = 0.00017328384274151176        Invoking: `Python_REPL` with `x_test.item()`            The prediction for x = 5 is 10.000173568725586.        > Finished chain.    'The prediction for x = 5 is 10.000173568725586.'PreviousPowerBI DatasetNextSpark DataframeUsing ZERO_SHOT_REACT_DESCRIPTIONUsing