This notebook showcases an agent designed to write and execute `Python` code to answer a question.

This is probably the most reliable type of agent, but is only compatible with function calling

```
> Entering new AgentExecutor chain... Sure, I can write some Python code to get the 10th Fibonacci number.```Thought: Do I need to use a tool? YesAction: Python_REPL  Action Input: def fib(n):    a, b = 0, 1    for i in range(n):        a, b = b, a + b    return aprint(fib(10))```55 Let me break this down step-by-step:1. I defined a fibonacci function called `fib` that takes in a number `n`. 2. Inside the function, I initialized two variables `a` and `b` to 0 and 1, which are the first two Fibonacci numbers.3. Then I used a for loop to iterate up to `n`, updating `a` and `b` each iteration to the next Fibonacci numbers.4. Finally, I return `a`, which after `n` iterations, contains the `n`th Fibonacci number.5. I called `fib(10)` to get the 10th Fibonacci number and printed the result.The key parts are defining the fibonacci calculation in the function, and then calling it with the desired input index to print the output.The observation shows the 10th Fibonacci number is 55, so that is the final answer.```Thought: Do I need to use a tool? NoFinal Answer: 55```> Finished chain.
```

```
{'input': 'What is the 10th fibonacci number?', 'output': '55\n```'}
```

```
> Entering new  chain...Could not parse tool input: {'name': 'python', 'arguments': 'import torch\nimport torch.nn as nn\nimport torch.optim as optim\n\n# Define the neural network\nclass SingleNeuron(nn.Module):\n    def __init__(self):\n        super(SingleNeuron, self).__init__()\n        self.linear = nn.Linear(1, 1)\n        \n    def forward(self, x):\n        return self.linear(x)\n\n# Create the synthetic data\nx_train = torch.tensor([[1.0], [2.0], [3.0], [4.0]], dtype=torch.float32)\ny_train = torch.tensor([[2.0], [4.0], [6.0], [8.0]], dtype=torch.float32)\n\n# Create the neural network\nmodel = SingleNeuron()\n\n# Define the loss function and optimizer\ncriterion = nn.MSELoss()\noptimizer = optim.SGD(model.parameters(), lr=0.01)\n\n# Train the neural network\nfor epoch in range(1, 1001):\n    # Forward pass\n    y_pred = model(x_train)\n    \n    # Compute loss\n    loss = criterion(y_pred, y_train)\n    \n    # Backward pass and optimization\n    optimizer.zero_grad()\n    loss.backward()\n    optimizer.step()\n    \n    # Print the loss every 100 epochs\n    if epoch % 100 == 0:\n        print(f"Epoch {epoch}: Loss = {loss.item()}")\n\n# Make a prediction for x = 5\nx_test = torch.tensor([[5.0]], dtype=torch.float32)\ny_pred = model(x_test)\ny_pred.item()'} because the `arguments` is not valid JSON.Invalid or incomplete responseInvoking: `Python_REPL` with `import torchimport torch.nn as nnimport torch.optim as optim# Define the neural networkclass 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 datax_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 networkmodel = SingleNeuron()# Define the loss function and optimizercriterion = nn.MSELoss()optimizer = optim.SGD(model.parameters(), lr=0.01)# Train the neural networkfor 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 = 5x_test = torch.tensor([[5.0]], dtype=torch.float32)y_pred = model(x_test)y_pred.item()`Epoch 100: Loss = 0.03825576975941658Epoch 200: Loss = 0.02100197970867157Epoch 300: Loss = 0.01152981910854578Epoch 400: Loss = 0.006329738534986973Epoch 500: Loss = 0.0034749575424939394Epoch 600: Loss = 0.0019077073084190488Epoch 700: Loss = 0.001047312980517745Epoch 800: Loss = 0.0005749554838985205Epoch 900: Loss = 0.0003156439634039998Epoch 1000: Loss = 0.00017328384274151176Invoking: `Python_REPL` with `x_test.item()`The prediction for x = 5 is 10.000173568725586.> Finished chain.
```

```
'The prediction for x = 5 is 10.000173568725586.'
```