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    "# Tensor - Core Data Structure\n",
    "\n",
    "Welcome to the Tensor module! This is where TinyTorch really begins. You'll implement the fundamental data structure that powers all ML systems.\n",
    "\n",
    "## Learning Goals\n",
    "- Understand tensors as N-dimensional arrays with ML-specific operations\n",
    "- Implement a complete Tensor class with arithmetic operations\n",
    "- Handle shape management, data types, and memory layout\n",
    "- Build the foundation for neural networks and automatic differentiation\n",
    "- Master the NBGrader workflow with comprehensive testing\n",
    "\n",
    "## Build → Use → Understand\n",
    "1. **Build**: Create the Tensor class with core operations\n",
    "2. **Use**: Perform tensor arithmetic and transformations\n",
    "3. **Understand**: How tensors form the foundation of ML systems"
   ]
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    "#| default_exp core.tensor\n",
    "\n",
    "#| export\n",
    "import numpy as np\n",
    "import sys\n",
    "from typing import Union, List, Tuple, Optional, Any"
   ]
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   "source": [
    "print(\"🔥 TinyTorch Tensor Module\")\n",
    "print(f\"NumPy version: {np.__version__}\")\n",
    "print(f\"Python version: {sys.version_info.major}.{sys.version_info.minor}\")\n",
    "print(\"Ready to build tensors!\")"
   ]
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   "source": [
    "## 📦 Where This Code Lives in the Final Package\n",
    "\n",
    "**Learning Side:** You work in `modules/source/01_tensor/tensor_dev.py`  \n",
    "**Building Side:** Code exports to `tinytorch.core.tensor`\n",
    "\n",
    "```python\n",
    "# Final package structure:\n",
    "from tinytorch.core.tensor import Tensor  # The foundation of everything!\n",
    "from tinytorch.core.activations import ReLU, Sigmoid, Tanh\n",
    "from tinytorch.core.layers import Dense, Conv2D\n",
    "```\n",
    "\n",
    "**Why this matters:**\n",
    "- **Learning:** Focused modules for deep understanding\n",
    "- **Production:** Proper organization like PyTorch's `torch.Tensor`\n",
    "- **Consistency:** All tensor operations live together in `core.tensor`\n",
    "- **Foundation:** Every other module depends on Tensor"
   ]
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   "source": [
    "## 🔧 DEVELOPMENT"
   ]
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    "## Step 1: What is a Tensor?\n",
    "\n",
    "### Definition\n",
    "A **tensor** is an N-dimensional array with ML-specific operations. Think of it as a container that can hold data in multiple dimensions:\n",
    "\n",
    "- **Scalar** (0D): A single number - `5.0`\n",
    "- **Vector** (1D): A list of numbers - `[1, 2, 3]`  \n",
    "- **Matrix** (2D): A 2D array - `[[1, 2], [3, 4]]`\n",
    "- **Higher dimensions**: 3D, 4D, etc. for images, video, batches\n",
    "\n",
    "### The Mathematical Foundation: From Scalars to Tensors\n",
    "Understanding tensors requires building from mathematical fundamentals:\n",
    "\n",
    "#### **Scalars (Rank 0)**\n",
    "- **Definition**: A single number with no direction\n",
    "- **Examples**: Temperature (25°C), mass (5.2 kg), probability (0.7)\n",
    "- **Operations**: Addition, multiplication, comparison\n",
    "- **ML Context**: Loss values, learning rates, regularization parameters\n",
    "\n",
    "#### **Vectors (Rank 1)**\n",
    "- **Definition**: An ordered list of numbers with direction and magnitude\n",
    "- **Examples**: Position [x, y, z], RGB color [255, 128, 0], word embedding [0.1, -0.5, 0.8]\n",
    "- **Operations**: Dot product, cross product, norm calculation\n",
    "- **ML Context**: Feature vectors, gradients, model parameters\n",
    "\n",
    "#### **Matrices (Rank 2)**\n",
    "- **Definition**: A 2D array organizing data in rows and columns\n",
    "- **Examples**: Image (height × width), weight matrix (input × output), covariance matrix\n",
    "- **Operations**: Matrix multiplication, transpose, inverse, eigendecomposition\n",
    "- **ML Context**: Linear layer weights, attention matrices, batch data\n",
    "\n",
    "#### **Higher-Order Tensors (Rank 3+)**\n",
    "- **Definition**: Multi-dimensional arrays extending matrices\n",
    "- **Examples**: \n",
    "  - **3D**: Video frames (time × height × width), RGB images (height × width × channels)\n",
    "  - **4D**: Image batches (batch × height × width × channels)\n",
    "  - **5D**: Video batches (batch × time × height × width × channels)\n",
    "- **Operations**: Tensor products, contractions, decompositions\n",
    "- **ML Context**: Convolutional features, RNN states, transformer attention\n",
    "\n",
    "### Why Tensors Matter in ML: The Computational Foundation\n",
    "\n",
    "#### **1. Unified Data Representation**\n",
    "Tensors provide a consistent way to represent all ML data:\n",
    "```python\n",
    "# All of these are tensors with different shapes\n",
    "scalar_loss = Tensor(0.5)              # Shape: ()\n",
    "feature_vector = Tensor([1, 2, 3])      # Shape: (3,)\n",
    "weight_matrix = Tensor([[1, 2], [3, 4]]) # Shape: (2, 2)\n",
    "image_batch = Tensor(np.random.rand(32, 224, 224, 3)) # Shape: (32, 224, 224, 3)\n",
    "```\n",
    "\n",
    "#### **2. Efficient Batch Processing**\n",
    "ML systems process multiple samples simultaneously:\n",
    "```python\n",
    "# Instead of processing one image at a time:\n",
    "for image in images:\n",
    "    result = model(image)  # Slow: 1000 separate operations\n",
    "\n",
    "# Process entire batch at once:\n",
    "batch_result = model(image_batch)  # Fast: 1 vectorized operation\n",
    "```\n",
    "\n",
    "#### **3. Hardware Acceleration**\n",
    "Modern hardware (GPUs, TPUs) excels at tensor operations:\n",
    "- **Parallel processing**: Multiple operations simultaneously\n",
    "- **Vectorization**: SIMD (Single Instruction, Multiple Data) operations\n",
    "- **Memory optimization**: Contiguous memory layout for cache efficiency\n",
    "\n",
    "#### **4. Automatic Differentiation**\n",
    "Tensors enable gradient computation through computational graphs:\n",
    "```python\n",
    "# Each tensor operation creates a node in the computation graph\n",
    "x = Tensor([1, 2, 3])\n",
    "y = x * 2          # Node: multiplication\n",
    "z = y + 1          # Node: addition\n",
    "loss = z.sum()     # Node: summation\n",
    "# Gradients flow backward through this graph\n",
    "```\n",
    "\n",
    "### Real-World Examples: Tensors in Action\n",
    "\n",
    "#### **Computer Vision**\n",
    "- **Grayscale image**: 2D tensor `(height, width)` - `(28, 28)` for MNIST\n",
    "- **Color image**: 3D tensor `(height, width, channels)` - `(224, 224, 3)` for RGB\n",
    "- **Image batch**: 4D tensor `(batch, height, width, channels)` - `(32, 224, 224, 3)`\n",
    "- **Video**: 5D tensor `(batch, time, height, width, channels)`\n",
    "\n",
    "#### **Natural Language Processing**\n",
    "- **Word embedding**: 1D tensor `(embedding_dim,)` - `(300,)` for Word2Vec\n",
    "- **Sentence**: 2D tensor `(sequence_length, embedding_dim)` - `(50, 768)` for BERT\n",
    "- **Batch of sentences**: 3D tensor `(batch, sequence_length, embedding_dim)`\n",
    "\n",
    "#### **Audio Processing**\n",
    "- **Audio signal**: 1D tensor `(time_steps,)` - `(16000,)` for 1 second at 16kHz\n",
    "- **Spectrogram**: 2D tensor `(time_frames, frequency_bins)`\n",
    "- **Batch of audio**: 3D tensor `(batch, time_steps, features)`\n",
    "\n",
    "#### **Time Series**\n",
    "- **Single series**: 2D tensor `(time_steps, features)`\n",
    "- **Multiple series**: 3D tensor `(batch, time_steps, features)`\n",
    "- **Multivariate forecasting**: 4D tensor `(batch, time_steps, features, predictions)`\n",
    "\n",
    "### Why Not Just Use NumPy?\n",
    "\n",
    "While we use NumPy internally, our Tensor class adds ML-specific functionality:\n",
    "\n",
    "#### **1. ML-Specific Operations**\n",
    "- **Gradient tracking**: For automatic differentiation (coming in Module 7)\n",
    "- **GPU support**: For hardware acceleration (future extension)\n",
    "- **Broadcasting semantics**: ML-friendly dimension handling\n",
    "\n",
    "#### **2. Consistent API**\n",
    "- **Type safety**: Predictable behavior across operations\n",
    "- **Error checking**: Clear error messages for debugging\n",
    "- **Integration**: Seamless work with other TinyTorch components\n",
    "\n",
    "#### **3. Educational Value**\n",
    "- **Conceptual clarity**: Understand what tensors really are\n",
    "- **Implementation insight**: See how frameworks work internally\n",
    "- **Debugging skills**: Trace through tensor operations step by step\n",
    "\n",
    "#### **4. Extensibility**\n",
    "- **Future features**: Ready for gradients, GPU, distributed computing\n",
    "- **Customization**: Add domain-specific operations\n",
    "- **Optimization**: Profile and optimize specific use cases\n",
    "\n",
    "### Performance Considerations: Building Efficient Tensors\n",
    "\n",
    "#### **Memory Layout**\n",
    "- **Contiguous arrays**: Better cache locality and performance\n",
    "- **Data types**: `float32` vs `float64` trade-offs\n",
    "- **Memory sharing**: Avoid unnecessary copies\n",
    "\n",
    "#### **Vectorization**\n",
    "- **SIMD operations**: Single Instruction, Multiple Data\n",
    "- **Broadcasting**: Efficient operations on different shapes\n",
    "- **Batch operations**: Process multiple samples simultaneously\n",
    "\n",
    "#### **Numerical Stability**\n",
    "- **Precision**: Balancing speed and accuracy\n",
    "- **Overflow/underflow**: Handling extreme values\n",
    "- **Gradient flow**: Maintaining numerical stability for training\n",
    "\n",
    "Let's start building our tensor foundation!"
   ]
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    "## 🧠 The Mathematical Foundation\n",
    "\n",
    "### Linear Algebra Refresher\n",
    "Tensors are generalizations of scalars, vectors, and matrices:\n",
    "\n",
    "```\n",
    "Scalar (0D): 5\n",
    "Vector (1D): [1, 2, 3]\n",
    "Matrix (2D): [[1, 2], [3, 4]]\n",
    "Tensor (3D): [[[1, 2], [3, 4]], [[5, 6], [7, 8]]]\n",
    "```\n",
    "\n",
    "### Why This Matters for Neural Networks\n",
    "- **Forward Pass**: Matrix multiplication between layers\n",
    "- **Batch Processing**: Multiple samples processed simultaneously\n",
    "- **Convolutions**: 3D operations on image data\n",
    "- **Gradients**: Derivatives computed across all dimensions\n",
    "\n",
    "### Connection to Real ML Systems\n",
    "Every major ML framework uses tensors:\n",
    "- **PyTorch**: `torch.Tensor`\n",
    "- **TensorFlow**: `tf.Tensor`\n",
    "- **JAX**: `jax.numpy.ndarray`\n",
    "- **TinyTorch**: `tinytorch.core.tensor.Tensor` (what we're building!)\n",
    "\n",
    "### Performance Considerations\n",
    "- **Memory Layout**: Contiguous arrays for cache efficiency\n",
    "- **Vectorization**: SIMD operations for speed\n",
    "- **Broadcasting**: Efficient operations on different shapes\n",
    "- **Type Consistency**: Avoiding unnecessary conversions"
   ]
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    "## Step 2: The Tensor Class Foundation\n",
    "\n",
    "### 🔍 The Wrapper Pattern: Why Every ML Framework Uses It\n",
    "\n",
    "Before we dive into implementation, let's understand a critical design pattern that ALL ML frameworks use:\n",
    "\n",
    "#### **The Two-Layer Architecture**\n",
    "```\n",
    "┌─────────────────────────────────────┐\n",
    "│   User-Facing API Layer (Python)    │  ← What you interact with\n",
    "│   - Tensor class                    │\n",
    "│   - Automatic differentiation       │\n",
    "│   - High-level operations          │\n",
    "├─────────────────────────────────────┤\n",
    "│   Efficient Compute Layer (C/C++)   │  ← What does the actual work\n",
    "│   - Optimized matrix operations     │\n",
    "│   - SIMD/vectorization             │\n",
    "│   - GPU kernels                    │\n",
    "└─────────────────────────────────────┘\n",
    "```\n",
    "\n",
    "#### **Why This Pattern?**\n",
    "1. **Performance**: Low-level languages (C/C++/CUDA) for speed\n",
    "2. **Usability**: High-level language (Python) for ease of use\n",
    "3. **Flexibility**: Add ML-specific features without modifying core compute\n",
    "4. **Portability**: Swap compute backends (CPU/GPU/TPU) transparently\n",
    "\n",
    "### Core Concept: Wrapping NumPy with ML Intelligence\n",
    "Our Tensor class wraps NumPy arrays with ML-specific functionality. This design pattern is used by all major ML frameworks:\n",
    "\n",
    "- **PyTorch**: `torch.Tensor` wraps ATen (C++ tensor library)\n",
    "- **TensorFlow**: `tf.Tensor` wraps Eigen (C++ linear algebra library)\n",
    "- **JAX**: `jax.numpy.ndarray` wraps XLA (Google's linear algebra compiler)\n",
    "- **TinyTorch**: `Tensor` wraps NumPy (Python's numerical computing library)\n",
    "\n",
    "### Design Requirements Analysis\n",
    "\n",
    "#### **1. Input Flexibility**\n",
    "Our tensor must handle diverse input types:\n",
    "```python\n",
    "# Scalars (Python numbers)\n",
    "t1 = Tensor(5)           # int → numpy array\n",
    "t2 = Tensor(3.14)        # float → numpy array\n",
    "\n",
    "# Lists (Python sequences)\n",
    "t3 = Tensor([1, 2, 3])   # list → numpy array\n",
    "t4 = Tensor([[1, 2], [3, 4]])  # nested list → 2D array\n",
    "\n",
    "# NumPy arrays (existing arrays)\n",
    "t5 = Tensor(np.array([1, 2, 3]))  # array → tensor wrapper\n",
    "```\n",
    "\n",
    "#### **2. Type Management**\n",
    "ML systems need consistent, predictable types:\n",
    "- **Default behavior**: Auto-detect appropriate types\n",
    "- **Explicit control**: Allow manual type specification\n",
    "- **Performance optimization**: Prefer `float32` over `float64`\n",
    "- **Memory efficiency**: Use appropriate precision\n",
    "\n",
    "#### **3. Property Access**\n",
    "Essential tensor properties for ML operations:\n",
    "- **Shape**: Dimensions for compatibility checking\n",
    "- **Size**: Total elements for memory estimation\n",
    "- **Data type**: For numerical computation planning\n",
    "- **Data access**: For integration with other libraries\n",
    "\n",
    "#### **4. Arithmetic Operations**\n",
    "Support for mathematical operations:\n",
    "- **Element-wise**: Addition, multiplication, subtraction, division\n",
    "- **Broadcasting**: Operations on different shapes\n",
    "- **Type promotion**: Consistent result types\n",
    "- **Error handling**: Clear messages for incompatible operations\n",
    "\n",
    "### Implementation Strategy\n",
    "\n",
    "#### **Memory Management**\n",
    "- **Copy vs. Reference**: When to copy data vs. share memory\n",
    "- **Type conversion**: Efficient dtype changes\n",
    "- **Contiguous layout**: Ensure optimal memory access patterns\n",
    "\n",
    "#### **Error Handling**\n",
    "- **Input validation**: Check for valid input types\n",
    "- **Shape compatibility**: Verify operations are mathematically valid\n",
    "- **Informative messages**: Help users debug issues quickly\n",
    "\n",
    "#### **Performance Optimization**\n",
    "- **Lazy evaluation**: Defer expensive operations when possible\n",
    "- **Vectorization**: Use NumPy's optimized operations\n",
    "- **Memory reuse**: Minimize unnecessary allocations\n",
    "\n",
    "### Learning Objectives for Implementation\n",
    "\n",
    "By implementing this Tensor class, you'll learn:\n",
    "1. **Wrapper pattern**: How to extend existing libraries\n",
    "2. **Type system design**: Managing data types in numerical computing\n",
    "3. **API design**: Creating intuitive, consistent interfaces\n",
    "4. **Performance considerations**: Balancing flexibility and speed\n",
    "5. **Error handling**: Providing helpful feedback to users\n",
    "\n",
    "### 📊 Understanding NumPy Data Types Before We Start\n",
    "\n",
    "Before implementing the constructor, let's understand NumPy's type system:\n",
    "\n",
    "#### **Common Data Types in ML**\n",
    "```python\n",
    "# Integer types (discrete values)\n",
    "'int8', 'int16', 'int32', 'int64'    # Signed integers\n",
    "'uint8', 'uint16', 'uint32', 'uint64' # Unsigned integers\n",
    "\n",
    "# Float types (continuous values)\n",
    "'float16' # Half precision (saves memory, less accurate)\n",
    "'float32' # Single precision (ML standard - good balance)\n",
    "'float64' # Double precision (high accuracy, more memory)\n",
    "```\n",
    "\n",
    "#### **Why We Prefer float32 in ML**\n",
    "- **Memory**: Uses half the memory of float64\n",
    "- **Speed**: Faster computation on GPUs\n",
    "- **Sufficient precision**: Good enough for most ML tasks\n",
    "- **Industry standard**: Used by PyTorch, TensorFlow by default\n",
    "\n",
    "Let's implement our tensor foundation!"
   ]
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    "#| export\n",
    "class Tensor:\n",
    "    \"\"\"\n",
    "    TinyTorch Tensor: N-dimensional array with ML operations.\n",
    "    \n",
    "    The fundamental data structure for all TinyTorch operations.\n",
    "    Wraps NumPy arrays with ML-specific functionality.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, data: Union[int, float, List, np.ndarray], dtype: Optional[str] = None):\n",
    "        \"\"\"\n",
    "        Create a new tensor from data.\n",
    "        \n",
    "        Args:\n",
    "            data: Input data (scalar, list, or numpy array)\n",
    "            dtype: Data type ('float32', 'int32', etc.). Defaults to auto-detect.\n",
    "            \n",
    "        TODO: Implement tensor creation with proper type handling.\n",
    "        \n",
    "        APPROACH:\n",
    "        1. Check the input data type using isinstance()\n",
    "           - If scalar (int/float): convert to numpy array\n",
    "           - If list: convert to numpy array\n",
    "           - If numpy array: use directly (but make a copy)\n",
    "        2. Handle the dtype parameter:\n",
    "           - If dtype is None: auto-detect the best type\n",
    "           - If dtype is specified: convert to that type\n",
    "        3. For auto-detection, prefer:\n",
    "           - int32 for integers (not int64)\n",
    "           - float32 for floats (not float64)\n",
    "        4. Store the final array in self._data\n",
    "        \n",
    "        EXAMPLE:\n",
    "        >>> t1 = Tensor(5)                    # Creates int32 array\n",
    "        >>> t2 = Tensor(3.14)                 # Creates float32 array\n",
    "        >>> t3 = Tensor([1, 2, 3])            # Creates int32 array\n",
    "        >>> t4 = Tensor([1.0, 2.0], 'float64') # Forces float64\n",
    "        >>> t5 = Tensor(np.array([1, 2]))     # Preserves numpy array\n",
    "        \n",
    "        HINTS:\n",
    "        - Use isinstance(data, (int, float)) to check for scalars\n",
    "        - Use isinstance(data, list) for lists\n",
    "        - Use isinstance(data, np.ndarray) for numpy arrays\n",
    "        - For dtype conversion: np.array(data, dtype=dtype)\n",
    "        - Remember: we prefer float32 over float64 for ML efficiency\n",
    "        \"\"\"\n",
    "        ### BEGIN SOLUTION\n",
    "        # Convert input to numpy array\n",
    "        if isinstance(data, (int, float, np.number)):\n",
    "            # Handle Python and NumPy scalars\n",
    "            if dtype is None:\n",
    "                # Auto-detect type: int for integers, float32 for floats\n",
    "                if isinstance(data, int) or (isinstance(data, np.number) and np.issubdtype(type(data), np.integer)):\n",
    "                    dtype = 'int32'\n",
    "                else:\n",
    "                    dtype = 'float32'\n",
    "            self._data = np.array(data, dtype=dtype)\n",
    "        elif isinstance(data, list):\n",
    "            # Let NumPy auto-detect type, then convert if needed\n",
    "            temp_array = np.array(data)\n",
    "            if dtype is None:\n",
    "                # Use NumPy's auto-detected type, but prefer float32 for floats\n",
    "                if temp_array.dtype == np.float64:\n",
    "                    dtype = 'float32'\n",
    "                else:\n",
    "                    dtype = str(temp_array.dtype)\n",
    "            self._data = np.array(data, dtype=dtype)\n",
    "        elif isinstance(data, np.ndarray):\n",
    "            # Already a numpy array\n",
    "            if dtype is None:\n",
    "                # Keep existing dtype, but prefer float32 for float64\n",
    "                if data.dtype == np.float64:\n",
    "                    dtype = 'float32'\n",
    "                else:\n",
    "                    dtype = str(data.dtype)\n",
    "            self._data = data.astype(dtype) if dtype != data.dtype else data.copy()\n",
    "        else:\n",
    "            # Try to convert unknown types\n",
    "            self._data = np.array(data, dtype=dtype)\n",
    "        ### END SOLUTION\n",
    "\n",
    "    @property\n",
    "    def data(self) -> np.ndarray:\n",
    "        \"\"\"\n",
    "        Access underlying numpy array.\n",
    "        \n",
    "        TODO: Return the stored numpy array.\n",
    "        \n",
    "        HINT: Return self._data (the array you stored in __init__)\n",
    "        \"\"\"\n",
    "        ### BEGIN SOLUTION\n",
    "        return self._data\n",
    "        ### END SOLUTION\n",
    "    \n",
    "    @property\n",
    "    def shape(self) -> Tuple[int, ...]:\n",
    "        \"\"\"\n",
    "        Get tensor shape.\n",
    "        \n",
    "        TODO: Return the shape of the stored numpy array.\n",
    "        \n",
    "        HINT: Use .shape attribute of the numpy array\n",
    "        EXAMPLE: Tensor([1, 2, 3]).shape should return (3,)\n",
    "        \"\"\"\n",
    "        ### BEGIN SOLUTION\n",
    "        return self._data.shape\n",
    "        ### END SOLUTION\n",
    "    \n",
    "    @property\n",
    "    def size(self) -> int:\n",
    "        \"\"\"\n",
    "        Get total number of elements.\n",
    "        \n",
    "        TODO: Return the total number of elements in the tensor.\n",
    "        \n",
    "        HINT: Use .size attribute of the numpy array\n",
    "        EXAMPLE: Tensor([1, 2, 3]).size should return 3\n",
    "        \"\"\"\n",
    "        ### BEGIN SOLUTION\n",
    "        return self._data.size\n",
    "        ### END SOLUTION\n",
    "    \n",
    "    @property\n",
    "    def dtype(self) -> np.dtype:\n",
    "        \"\"\"\n",
    "        Get data type as numpy dtype.\n",
    "        \n",
    "        TODO: Return the data type of the stored numpy array.\n",
    "        \n",
    "        HINT: Use .dtype attribute of the numpy array\n",
    "        EXAMPLE: Tensor([1, 2, 3]).dtype should return dtype('int32')\n",
    "        \"\"\"\n",
    "        ### BEGIN SOLUTION\n",
    "        return self._data.dtype\n",
    "        ### END SOLUTION\n",
    "    \n",
    "    def __repr__(self) -> str:\n",
    "        \"\"\"\n",
    "        String representation.\n",
    "        \n",
    "        TODO: Create a clear string representation of the tensor.\n",
    "        \n",
    "        APPROACH:\n",
    "        1. Convert the numpy array to a list for readable output\n",
    "        2. Include the shape and dtype information\n",
    "        3. Format: \"Tensor([data], shape=shape, dtype=dtype)\"\n",
    "        \n",
    "        EXAMPLE:\n",
    "        Tensor([1, 2, 3]) → \"Tensor([1, 2, 3], shape=(3,), dtype=int32)\"\n",
    "        \n",
    "        HINTS:\n",
    "        - Use .tolist() to convert numpy array to list\n",
    "        - Include shape and dtype information\n",
    "        - Keep format consistent and readable\n",
    "        \"\"\"\n",
    "        ### BEGIN SOLUTION\n",
    "        return f\"Tensor({self._data.tolist()}, shape={self.shape}, dtype={self.dtype})\"\n",
    "        ### END SOLUTION\n",
    "\n",
    "    def add(self, other: 'Tensor') -> 'Tensor':\n",
    "        \"\"\"\n",
    "        Add two tensors element-wise.\n",
    "        \n",
    "        TODO: Implement tensor addition.\n",
    "        \n",
    "        APPROACH:\n",
    "        1. Add the numpy arrays using +\n",
    "        2. Return a new Tensor with the result\n",
    "        3. Handle broadcasting automatically\n",
    "        \n",
    "        EXAMPLE:\n",
    "        Tensor([1, 2]) + Tensor([3, 4]) → Tensor([4, 6])\n",
    "        \n",
    "        HINTS:\n",
    "        - Use self._data + other._data\n",
    "        - Return Tensor(result)\n",
    "        - NumPy handles broadcasting automatically\n",
    "        \"\"\"\n",
    "        ### BEGIN SOLUTION\n",
    "        result = self._data + other._data\n",
    "        return Tensor(result)\n",
    "        ### END SOLUTION\n",
    "\n",
    "    def multiply(self, other: 'Tensor') -> 'Tensor':\n",
    "        \"\"\"\n",
    "        Multiply two tensors element-wise.\n",
    "        \n",
    "        TODO: Implement tensor multiplication.\n",
    "        \n",
    "        APPROACH:\n",
    "        1. Multiply the numpy arrays using *\n",
    "        2. Return a new Tensor with the result\n",
    "        3. Handle broadcasting automatically\n",
    "        \n",
    "        EXAMPLE:\n",
    "        Tensor([1, 2]) * Tensor([3, 4]) → Tensor([3, 8])\n",
    "        \n",
    "        HINTS:\n",
    "        - Use self._data * other._data\n",
    "        - Return Tensor(result)\n",
    "        - This is element-wise, not matrix multiplication\n",
    "        \"\"\"\n",
    "        ### BEGIN SOLUTION\n",
    "        result = self._data * other._data\n",
    "        return Tensor(result)\n",
    "        ### END SOLUTION\n",
    "\n",
    "    def __add__(self, other: Union['Tensor', int, float]) -> 'Tensor':\n",
    "        \"\"\"\n",
    "        Addition operator overload for natural syntax: tensor + other.\n",
    "        \n",
    "        TODO: Implement the + operator to enable natural addition syntax for tensors.\n",
    "        \n",
    "        APPROACH:\n",
    "        1. Check if other is a Tensor or scalar (int/float)\n",
    "        2. If scalar, convert to Tensor for uniform handling\n",
    "        3. Delegate to the add() method for actual computation\n",
    "        4. Return the resulting Tensor\n",
    "        \n",
    "        EXAMPLE:\n",
    "        >>> a = Tensor([1, 2, 3])\n",
    "        >>> b = Tensor([4, 5, 6])\n",
    "        >>> c = a + b  # Element-wise addition\n",
    "        >>> print(c.data)  # [5, 7, 9]\n",
    "        >>> d = a + 10  # Broadcasting with scalar\n",
    "        >>> print(d.data)  # [11, 12, 13]\n",
    "        \n",
    "        HINTS:\n",
    "        - Use isinstance(other, Tensor) to check type\n",
    "        - Convert scalars with Tensor(other)\n",
    "        - Delegate to self.add() for consistency\n",
    "        - Python automatically calls this for + operator\n",
    "        \n",
    "        LEARNING CONNECTIONS:\n",
    "        - This is how torch.Tensor implements natural syntax\n",
    "        - Operator overloading makes math operations intuitive\n",
    "        - Broadcasting happens automatically in the add() method\n",
    "        \"\"\"\n",
    "        ### BEGIN SOLUTION\n",
    "        if isinstance(other, Tensor):\n",
    "            return self.add(other)\n",
    "        else:\n",
    "            return self.add(Tensor(other))\n",
    "        ### END SOLUTION\n",
    "\n",
    "    def __mul__(self, other: Union['Tensor', int, float]) -> 'Tensor':\n",
    "        \"\"\"\n",
    "        Multiplication operator overload for natural syntax: tensor * other.\n",
    "        \n",
    "        TODO: Implement the * operator for element-wise multiplication (Hadamard product).\n",
    "        \n",
    "        APPROACH:\n",
    "        1. Check if other is a Tensor or scalar (int/float)\n",
    "        2. If scalar, convert to Tensor for uniform handling\n",
    "        3. Delegate to the multiply() method for computation\n",
    "        4. Return the resulting Tensor\n",
    "        \n",
    "        EXAMPLE:\n",
    "        >>> a = Tensor([2, 3, 4])\n",
    "        >>> b = Tensor([5, 6, 7])\n",
    "        >>> c = a * b  # Element-wise multiplication\n",
    "        >>> print(c.data)  # [10, 18, 28]\n",
    "        >>> d = a * 3  # Scalar multiplication\n",
    "        >>> print(d.data)  # [6, 9, 12]\n",
    "        \n",
    "        HINTS:\n",
    "        - This is element-wise, NOT matrix multiplication\n",
    "        - For matrix multiplication, use @ operator (matmul)\n",
    "        - Scalar multiplication broadcasts automatically\n",
    "        - Delegate to self.multiply() for consistency\n",
    "        \n",
    "        LEARNING CONNECTIONS:\n",
    "        - In PyTorch: * is element-wise, @ is matrix multiplication\n",
    "        - Neural networks use this for element-wise activations\n",
    "        - Scalar multiplication is used for learning rate scaling\n",
    "        \"\"\"\n",
    "        ### BEGIN SOLUTION\n",
    "        if isinstance(other, Tensor):\n",
    "            return self.multiply(other)\n",
    "        else:\n",
    "            return self.multiply(Tensor(other))\n",
    "        ### END SOLUTION\n",
    "\n",
    "    def __sub__(self, other: Union['Tensor', int, float]) -> 'Tensor':\n",
    "        \"\"\"\n",
    "        Subtraction operator overload for natural syntax: tensor - other.\n",
    "        \n",
    "        TODO: Implement the - operator for element-wise subtraction.\n",
    "        \n",
    "        APPROACH:\n",
    "        1. Check if other is a Tensor or scalar\n",
    "        2. Access the underlying NumPy arrays with ._data\n",
    "        3. Perform subtraction using NumPy's broadcasting\n",
    "        4. Wrap the result in a new Tensor and return\n",
    "        \n",
    "        EXAMPLE:\n",
    "        >>> a = Tensor([10, 20, 30])\n",
    "        >>> b = Tensor([3, 5, 7])\n",
    "        >>> c = a - b  # Element-wise subtraction\n",
    "        >>> print(c.data)  # [7, 15, 23]\n",
    "        >>> d = a - 5  # Broadcasting with scalar\n",
    "        >>> print(d.data)  # [5, 15, 25]\n",
    "        \n",
    "        HINTS:\n",
    "        - Access data with self._data and other._data\n",
    "        - NumPy handles broadcasting automatically\n",
    "        - Always return a new Tensor (immutability)\n",
    "        - Handle both Tensor and scalar cases\n",
    "        \n",
    "        LEARNING CONNECTIONS:\n",
    "        - Used in gradient computation (loss - target)\n",
    "        - Batch normalization uses mean subtraction\n",
    "        - Residual connections compute (output - input)\n",
    "        \"\"\"\n",
    "        ### BEGIN SOLUTION\n",
    "        if isinstance(other, Tensor):\n",
    "            result = self._data - other._data\n",
    "        else:\n",
    "            result = self._data - other\n",
    "        return Tensor(result)\n",
    "        ### END SOLUTION\n",
    "\n",
    "    def __truediv__(self, other: Union['Tensor', int, float]) -> 'Tensor':\n",
    "        \"\"\"\n",
    "        Division operator overload for natural syntax: tensor / other.\n",
    "        \n",
    "        TODO: Implement the / operator for element-wise division.\n",
    "        \n",
    "        APPROACH:\n",
    "        1. Check if other is a Tensor or scalar\n",
    "        2. Access the underlying NumPy arrays\n",
    "        3. Perform division (watch for division by zero!)\n",
    "        4. Wrap the result in a new Tensor and return\n",
    "        \n",
    "        EXAMPLE:\n",
    "        >>> a = Tensor([10, 20, 30])\n",
    "        >>> b = Tensor([2, 4, 5])\n",
    "        >>> c = a / b  # Element-wise division\n",
    "        >>> print(c.data)  # [5.0, 5.0, 6.0]\n",
    "        >>> d = a / 10  # Scalar division\n",
    "        >>> print(d.data)  # [1.0, 2.0, 3.0]\n",
    "        \n",
    "        HINTS:\n",
    "        - NumPy handles division by zero → inf/nan\n",
    "        - Results are always float type\n",
    "        - Consider using safe_divide() for production\n",
    "        - Broadcasting rules apply here too\n",
    "        \n",
    "        LEARNING CONNECTIONS:\n",
    "        - Normalization often uses division (x / std)\n",
    "        - Softmax involves division by sum\n",
    "        - Learning rate scheduling uses division\n",
    "        - Numerical stability is critical in ML\n",
    "        \"\"\"\n",
    "        ### BEGIN SOLUTION\n",
    "        if isinstance(other, Tensor):\n",
    "            result = self._data / other._data\n",
    "        else:\n",
    "            result = self._data / other\n",
    "        return Tensor(result)\n",
    "        ### END SOLUTION\n",
    "\n",
    "    def mean(self) -> 'Tensor':\n",
    "        \"\"\"Computes the mean of the tensor's elements.\"\"\"\n",
    "        return Tensor(np.mean(self.data))\n",
    "\n",
    "    # --- Matmul ---\n",
    "    def matmul(self, other: 'Tensor') -> 'Tensor':\n",
    "        \"\"\"\n",
    "        Perform matrix multiplication between two tensors.\n",
    "        \n",
    "        TODO: Implement matrix multiplication.\n",
    "        \n",
    "        APPROACH:\n",
    "        1. Use np.matmul() to perform matrix multiplication\n",
    "        2. Return a new Tensor with the result\n",
    "        3. Handle broadcasting automatically\n",
    "        \n",
    "        EXAMPLE:\n",
    "        Tensor([[1, 2], [3, 4]]) @ Tensor([[5, 6], [7, 8]]) → Tensor([[19, 22], [43, 50]])\n",
    "        \n",
    "        HINTS:\n",
    "        - Use np.matmul(self._data, other._data)\n",
    "        - Return Tensor(result)\n",
    "        - This is matrix multiplication, not element-wise multiplication\n",
    "        \"\"\"\n",
    "        ### BEGIN SOLUTION\n",
    "        result = np.matmul(self._data, other._data)\n",
    "        return Tensor(result)\n",
    "        ### END SOLUTION\n",
    "    \n",
    "    # --- Error Handling ---\n",
    "    def validate_operation(self, other: 'Tensor', operation: str) -> None:\n",
    "        \"\"\"\n",
    "        Validate tensor compatibility for operations with informative error messages.\n",
    "        \n",
    "        TODO: Implement validation for tensor operations that checks shape compatibility\n",
    "        and provides educational error messages when operations would fail.\n",
    "        \n",
    "        APPROACH:\n",
    "        1. Check if the operation is element-wise or matrix multiplication\n",
    "        2. For element-wise ops, verify broadcasting compatibility\n",
    "        3. For matmul, verify matrix dimension alignment\n",
    "        4. Raise informative ValueError with hints if incompatible\n",
    "        \n",
    "        EXAMPLE:\n",
    "        >>> a = Tensor([[1, 2, 3]])  # Shape: (1, 3)\n",
    "        >>> b = Tensor([[1], [2]])   # Shape: (2, 1)\n",
    "        >>> a.validate_operation(b, 'add')  # Should pass (broadcasting)\n",
    "        >>> a.validate_operation(b, 'matmul')  # Should raise with helpful message\n",
    "        \n",
    "        HINTS:\n",
    "        - Use np.broadcast_shapes() to check broadcasting compatibility\n",
    "        - For matmul, check that self.shape[-1] == other.shape[-2]\n",
    "        - Include shapes in error messages to help debugging\n",
    "        - Suggest fixes in the error message\n",
    "        \n",
    "        LEARNING CONNECTIONS:\n",
    "        - PyTorch raises similar errors with shape information\n",
    "        - Good error messages accelerate learning and debugging\n",
    "        - Understanding shape compatibility is crucial for ML\n",
    "        \"\"\"\n",
    "        ### BEGIN SOLUTION\n",
    "        if operation in ['add', 'mul', 'sub', 'div']:\n",
    "            # Check broadcasting compatibility for element-wise operations\n",
    "            try:\n",
    "                np.broadcast_shapes(self.shape, other.shape)\n",
    "            except ValueError:\n",
    "                raise ValueError(\n",
    "                    f\"Cannot {operation} tensors with incompatible shapes: \"\n",
    "                    f\"{self.shape} and {other.shape}. \"\n",
    "                    f\"Hint: Shapes must be broadcastable. Either dimensions match \"\n",
    "                    f\"or one of them is 1. Consider reshaping with tensor.reshape().\"\n",
    "                )\n",
    "        elif operation == 'matmul':\n",
    "            # Check matrix multiplication compatibility\n",
    "            if len(self.shape) == 0 or len(other.shape) == 0:\n",
    "                raise ValueError(\n",
    "                    f\"Cannot perform matrix multiplication with scalar tensors. \"\n",
    "                    f\"Shapes: {self.shape} @ {other.shape}. \"\n",
    "                    f\"Hint: Use regular multiplication (*) for scalar operations.\"\n",
    "                )\n",
    "            if self.shape[-1] != other.shape[-2 if len(other.shape) > 1 else 0]:\n",
    "                raise ValueError(\n",
    "                    f\"Cannot perform matrix multiplication with incompatible shapes: \"\n",
    "                    f\"{self.shape} @ {other.shape}. \"\n",
    "                    f\"Hint: For A @ B, A's last dimension ({self.shape[-1]}) must match \"\n",
    "                    f\"B's second-to-last dimension ({other.shape[-2 if len(other.shape) > 1 else 0]}). \"\n",
    "                    f\"Consider transposing with tensor.T or reshaping.\"\n",
    "                )\n",
    "        ### END SOLUTION\n",
    "    \n",
    "    def safe_divide(self, other: Union['Tensor', int, float]) -> 'Tensor':\n",
    "        \"\"\"\n",
    "        Perform division with zero-division protection and informative warnings.\n",
    "        \n",
    "        TODO: Implement safe division that handles division by zero gracefully\n",
    "        and provides educational feedback about numerical stability.\n",
    "        \n",
    "        APPROACH:\n",
    "        1. Check for zero values in the divisor\n",
    "        2. Use np.where to handle zero divisions appropriately  \n",
    "        3. Optionally warn about near-zero values that might cause instability\n",
    "        4. Return result with inf/nan handled according to NumPy conventions\n",
    "        \n",
    "        EXAMPLE:\n",
    "        >>> a = Tensor([1, 2, 3, 4])\n",
    "        >>> b = Tensor([2, 0, 1, 0])\n",
    "        >>> result = a.safe_divide(b)  # Handles zeros gracefully\n",
    "        >>> print(result.data)  # [0.5, inf, 3.0, inf] with warning\n",
    "        \n",
    "        HINTS:\n",
    "        - Use np.where(divisor == 0, np.inf, normal_division)\n",
    "        - Consider np.errstate(divide='ignore') context manager\n",
    "        - Check for small values with np.abs(divisor) < epsilon\n",
    "        - Return informative warning messages for learning\n",
    "        \n",
    "        LEARNING CONNECTIONS:\n",
    "        - Numerical stability is critical in ML (gradient explosions)\n",
    "        - Many ML bugs come from division by zero in normalizations\n",
    "        - Production systems need robust error handling\n",
    "        \"\"\"\n",
    "        ### BEGIN SOLUTION\n",
    "        other_data = other._data if isinstance(other, Tensor) else other\n",
    "        \n",
    "        # Check for exact zeros\n",
    "        if np.any(other_data == 0):\n",
    "            import warnings\n",
    "            warnings.warn(\n",
    "                \"Division by zero detected! Results will contain inf values. \"\n",
    "                \"In ML, this often happens in normalization or loss calculations. \"\n",
    "                \"Consider adding a small epsilon (e.g., 1e-8) to prevent instability.\",\n",
    "                category=RuntimeWarning\n",
    "            )\n",
    "        \n",
    "        # Check for near-zero values that might cause instability\n",
    "        epsilon = 1e-10\n",
    "        if np.any(np.abs(other_data) < epsilon):\n",
    "            import warnings\n",
    "            warnings.warn(\n",
    "                f\"Near-zero divisor detected (values < {epsilon}). \"\n",
    "                \"This may cause numerical instability in gradients. \"\n",
    "                \"Consider using stabilized versions of your operations.\",\n",
    "                category=RuntimeWarning\n",
    "            )\n",
    "        \n",
    "        # Perform division with NumPy's default handling (inf for division by zero)\n",
    "        with np.errstate(divide='ignore', invalid='ignore'):\n",
    "            result = self._data / other_data\n",
    "        \n",
    "        return Tensor(result)\n",
    "        ### END SOLUTION"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a354d1b5",
   "metadata": {
    "cell_marker": "\"\"\""
   },
   "source": [
    "### 🎯 Checkpoint: Basic Tensor Creation\n",
    "Before moving on, make sure you can:\n",
    "```python\n",
    "# Create different tensor types\n",
    "scalar = Tensor(5)              # 0D tensor\n",
    "vector = Tensor([1, 2, 3])      # 1D tensor  \n",
    "matrix = Tensor([[1, 2], [3, 4]]) # 2D tensor\n",
    "\n",
    "# Check their properties\n",
    "print(f\"Scalar shape: {scalar.shape}\")  # ()\n",
    "print(f\"Vector shape: {vector.shape}\")  # (3,)\n",
    "print(f\"Matrix shape: {matrix.shape}\")  # (2, 2)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df030d32",
   "metadata": {
    "cell_marker": "\"\"\""
   },
   "source": [
    "## Step 3: Understanding Broadcasting for Arithmetic Operations\n",
    "\n",
    "### 📊 Broadcasting: The Magic of Flexible Operations\n",
    "\n",
    "Broadcasting allows operations between tensors of different shapes without explicit reshaping. It's what makes code like `matrix + vector` or `tensor * scalar` work seamlessly.\n",
    "\n",
    "#### **Broadcasting Rules (Simple to Complex)**\n",
    "\n",
    "**Rule 1: Scalar Broadcasting**\n",
    "```\n",
    "Scalar + Any Shape = Element-wise Operation\n",
    "    5    +  [1,2,3]  =  [6,7,8]\n",
    "```\n",
    "\n",
    "**Rule 2: Vector Broadcasting**\n",
    "```\n",
    "Matrix + Vector (matching last dimension)\n",
    "[[1,2,3]     [10,20,30]     [[11,22,33]\n",
    " [4,5,6]]  +             =    [14,25,36]]\n",
    "```\n",
    "\n",
    "**Rule 3: Dimension Alignment**\n",
    "```\n",
    "Shapes are aligned from the right:\n",
    "(3, 4) and (4,)  → Compatible (4 matches)\n",
    "(3, 4) and (3,)  → Not compatible (3 doesn't match 4)\n",
    "```\n",
    "\n",
    "#### **Visual Examples**\n",
    "```python\n",
    "# Scalar broadcasting\n",
    "[1, 2, 3] + 5 = [1+5, 2+5, 3+5] = [6, 7, 8]\n",
    "\n",
    "# Row broadcasting  \n",
    "[[1, 2]    [10, 20]   [[1+10, 2+20]     [[11, 22]\n",
    " [3, 4]] +          =  [3+10, 4+20]]  =  [13, 24]]\n",
    "\n",
    "# Column broadcasting\n",
    "[[1, 2]    [[10]    [[1+10, 2+10]     [[11, 12]\n",
    " [3, 4]] +  [20]] =  [3+20, 4+20]]  =  [23, 24]]\n",
    "```\n",
    "\n",
    "### Why Broadcasting Matters in ML\n",
    "- **Batch operations**: Add bias to all samples in a batch\n",
    "- **Normalization**: Subtract mean from all features\n",
    "- **Scaling**: Multiply by weights across dimensions\n",
    "- **Efficiency**: No memory copying, just smart iteration\n",
    "\n",
    "With this understanding, the arithmetic operations in our Tensor class will automatically support broadcasting through NumPy!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bd1d01d",
   "metadata": {
    "cell_marker": "\"\"\""
   },
   "source": [
    "### 🧪 Unit Test: Tensor Creation\n",
    "\n",
    "Let's test your tensor creation implementation right away! This gives you immediate feedback on whether your `__init__` method works correctly.\n",
    "\n",
    "**This is a unit test** - it tests one specific function (tensor creation) in isolation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3fbade8",
   "metadata": {
    "nbgrader": {
     "grade": true,
     "grade_id": "test-tensor-creation-immediate",
     "locked": true,
     "points": 5,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# Test tensor creation immediately after implementation\n",
    "print(\"🔬 Unit Test: Tensor Creation...\")\n",
    "\n",
    "# Test basic tensor creation\n",
    "try:\n",
    "    # Test scalar\n",
    "    scalar = Tensor(5.0)\n",
    "    assert hasattr(scalar, '_data'), \"Tensor should have _data attribute\"\n",
    "    assert scalar._data.shape == (), f\"Scalar should have shape (), got {scalar._data.shape}\"\n",
    "    print(\"✅ Scalar creation works\")\n",
    "    \n",
    "    # Test vector\n",
    "    vector = Tensor([1, 2, 3])\n",
    "    assert vector._data.shape == (3,), f\"Vector should have shape (3,), got {vector._data.shape}\"\n",
    "    print(\"✅ Vector creation works\")\n",
    "    \n",
    "    # Test matrix\n",
    "    matrix = Tensor([[1, 2], [3, 4]])\n",
    "    assert matrix._data.shape == (2, 2), f\"Matrix should have shape (2, 2), got {matrix._data.shape}\"\n",
    "    print(\"✅ Matrix creation works\")\n",
    "    \n",
    "    print(\"📈 Progress: Tensor Creation ✓\")\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"❌ Tensor creation test failed: {e}\")\n",
    "    raise\n",
    "\n",
    "print(\"🎯 Tensor creation behavior:\")\n",
    "print(\"   Converts data to NumPy arrays\")\n",
    "print(\"   Preserves shape and data type\")\n",
    "print(\"   Stores in _data attribute\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b74cbc1",
   "metadata": {
    "cell_marker": "\"\"\""
   },
   "source": [
    "### 🧪 Unit Test: Tensor Properties\n",
    "\n",
    "Now let's test that your tensor properties work correctly. This tests the @property methods you implemented.\n",
    "\n",
    "**This is a unit test** - it tests specific properties (shape, size, dtype, data) in isolation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c02437ea",
   "metadata": {
    "nbgrader": {
     "grade": true,
     "grade_id": "test-tensor-properties-immediate",
     "locked": true,
     "points": 5,
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   "outputs": [],
   "source": [
    "# Test tensor properties immediately after implementation\n",
    "print(\"🔬 Unit Test: Tensor Properties...\")\n",
    "\n",
    "# Test properties with simple examples\n",
    "try:\n",
    "    # Test with a simple matrix\n",
    "    tensor = Tensor([[1, 2, 3], [4, 5, 6]])\n",
    "    \n",
    "    # Test shape property\n",
    "    assert tensor.shape == (2, 3), f\"Shape should be (2, 3), got {tensor.shape}\"\n",
    "    print(\"✅ Shape property works\")\n",
    "    \n",
    "    # Test size property\n",
    "    assert tensor.size == 6, f\"Size should be 6, got {tensor.size}\"\n",
    "    print(\"✅ Size property works\")\n",
    "    \n",
    "    # Test data property\n",
    "    assert np.array_equal(tensor.data, np.array([[1, 2, 3], [4, 5, 6]])), \"Data property should return numpy array\"\n",
    "    print(\"✅ Data property works\")\n",
    "    \n",
    "    # Test dtype property\n",
    "    assert tensor.dtype in [np.int32, np.int64], f\"Dtype should be int32 or int64, got {tensor.dtype}\"\n",
    "    print(\"✅ Dtype property works\")\n",
    "    \n",
    "    print(\"📈 Progress: Tensor Properties ✓\")\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"❌ Tensor properties test failed: {e}\")\n",
    "    raise\n",
    "\n",
    "print(\"🎯 Tensor properties behavior:\")\n",
    "print(\"   shape: Returns tuple of dimensions\")\n",
    "print(\"   size: Returns total number of elements\")\n",
    "print(\"   data: Returns underlying NumPy array\")\n",
    "print(\"   dtype: Returns NumPy data type\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ceb7f26",
   "metadata": {
    "cell_marker": "\"\"\""
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   "source": [
    "### 🧪 Unit Test: Tensor Arithmetic\n",
    "\n",
    "Let's test your tensor arithmetic operations. This tests the __add__, __mul__, __sub__, __truediv__ methods.\n",
    "\n",
    "**This is a unit test** - it tests specific arithmetic operations in isolation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fcf0369b",
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    "nbgrader": {
     "grade": true,
     "grade_id": "test-tensor-arithmetic-immediate",
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   "source": [
    "# Test tensor arithmetic immediately after implementation\n",
    "print(\"🔬 Unit Test: Tensor Arithmetic...\")\n",
    "\n",
    "# Test basic arithmetic with simple examples\n",
    "try:\n",
    "    # Test addition\n",
    "    a = Tensor([1, 2, 3])\n",
    "    b = Tensor([4, 5, 6])\n",
    "    result = a + b\n",
    "    expected = np.array([5, 7, 9])\n",
    "    assert np.array_equal(result.data, expected), f\"Addition failed: expected {expected}, got {result.data}\"\n",
    "    print(\"✅ Addition works\")\n",
    "    \n",
    "    # Test scalar addition\n",
    "    result_scalar = a + 10\n",
    "    expected_scalar = np.array([11, 12, 13])\n",
    "    assert np.array_equal(result_scalar.data, expected_scalar), f\"Scalar addition failed: expected {expected_scalar}, got {result_scalar.data}\"\n",
    "    print(\"✅ Scalar addition works\")\n",
    "    \n",
    "    # Test multiplication\n",
    "    result_mul = a * b\n",
    "    expected_mul = np.array([4, 10, 18])\n",
    "    assert np.array_equal(result_mul.data, expected_mul), f\"Multiplication failed: expected {expected_mul}, got {result_mul.data}\"\n",
    "    print(\"✅ Multiplication works\")\n",
    "    \n",
    "    # Test scalar multiplication\n",
    "    result_scalar_mul = a * 2\n",
    "    expected_scalar_mul = np.array([2, 4, 6])\n",
    "    assert np.array_equal(result_scalar_mul.data, expected_scalar_mul), f\"Scalar multiplication failed: expected {expected_scalar_mul}, got {result_scalar_mul.data}\"\n",
    "    print(\"✅ Scalar multiplication works\")\n",
    "    \n",
    "    print(\"📈 Progress: Tensor Arithmetic ✓\")\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"❌ Tensor arithmetic test failed: {e}\")\n",
    "    raise\n",
    "\n",
    "print(\"🎯 Tensor arithmetic behavior:\")\n",
    "print(\"   Element-wise operations on tensors\")\n",
    "print(\"   Broadcasting with scalars\")\n",
    "print(\"   Returns new Tensor objects\")"
   ]
  },
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   "source": [
    "Congratulations! You've successfully implemented the core Tensor class for TinyTorch:\n",
    "\n",
    "### What You've Accomplished\n",
    "✅ **Tensor Creation**: Handle scalars, vectors, matrices, and higher-dimensional arrays  \n",
    "✅ **Data Types**: Proper dtype handling with auto-detection and conversion  \n",
    "✅ **Properties**: Shape, size, dtype, and data access  \n",
    "✅ **Arithmetic**: Addition, multiplication, subtraction, division  \n",
    "✅ **Operators**: Natural Python syntax with `+`, `-`, `*`, `/`  \n",
    "✅ **Broadcasting**: Automatic shape compatibility like NumPy  \n",
    "\n",
    "### Key Concepts You've Learned\n",
    "- **Tensors** are the fundamental data structure for ML systems\n",
    "- **NumPy backend** provides efficient computation with ML-friendly API\n",
    "- **Operator overloading** makes tensor operations feel natural\n",
    "- **Broadcasting** enables flexible operations between different shapes\n",
    "- **Type safety** ensures consistent behavior across operations\n",
    "\n",
    "### Next Steps\n",
    "1. **Export your code**: `tito package nbdev --export 01_tensor`\n",
    "2. **Test your implementation**: `tito module test 01_tensor`\n",
    "3. **Use your tensors**: \n",
    "   ```python\n",
    "   from tinytorch.core.tensor import Tensor\n",
    "   t = Tensor([1, 2, 3])\n",
    "   print(t + 5)  # Your tensor in action!\n",
    "   ```\n",
    "4. **Move to Module 2**: Start building activation functions!\n",
    "\n",
    "**Ready for the next challenge?** Let's add the mathematical functions that make neural networks powerful!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeb499fa",
   "metadata": {
    "cell_marker": "\"\"\"",
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   "source": [
    "### 🧪 Unit Test: Tensor Creation\n",
    "\n",
    "This test validates your `Tensor` class constructor, ensuring it correctly handles scalars, vectors, matrices, and higher-dimensional arrays with proper shape detection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4e8cc6b",
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   "source": [
    "def test_unit_tensor_creation():\n",
    "    \"\"\"Comprehensive test of tensor creation with all data types and shapes.\"\"\"\n",
    "    print(\"🔬 Testing comprehensive tensor creation...\")\n",
    "    \n",
    "    # Test scalar creation\n",
    "    scalar_int = Tensor(42)\n",
    "    assert scalar_int.shape == ()\n",
    "    \n",
    "    # Test vector creation\n",
    "    vector_int = Tensor([1, 2, 3])\n",
    "    assert vector_int.shape == (3,)\n",
    "\n",
    "    # Test matrix creation\n",
    "    matrix_2x2 = Tensor([[1, 2], [3, 4]])\n",
    "    assert matrix_2x2.shape == (2, 2)\n",
    "    print(\"✅ Tensor creation tests passed!\")\n",
    "\n",
    "# Run the test\n",
    "test_unit_tensor_creation()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9624c0a7",
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   "source": [
    "### 🧪 Unit Test: Tensor Properties\n",
    "\n",
    "This test validates your tensor property methods (shape, size, dtype, data), ensuring they correctly reflect the tensor's dimensional structure and data characteristics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54cef0a2",
   "metadata": {},
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   "source": [
    "def test_unit_tensor_properties():\n",
    "    \"\"\"Comprehensive test of tensor properties (shape, size, dtype, data access).\"\"\"\n",
    "    print(\"🔬 Testing comprehensive tensor properties...\")\n",
    "\n",
    "    tensor = Tensor([[1, 2, 3], [4, 5, 6]])\n",
    "    \n",
    "    # Test shape property\n",
    "    assert tensor.shape == (2, 3)\n",
    "    \n",
    "    # Test size property\n",
    "    assert tensor.size == 6\n",
    "    \n",
    "    # Test data property\n",
    "    assert np.array_equal(tensor.data, np.array([[1, 2, 3], [4, 5, 6]]))\n",
    "    \n",
    "    # Test dtype property\n",
    "    assert tensor.dtype in [np.int32, np.int64]\n",
    "    print(\"✅ Tensor properties tests passed!\")\n",
    "\n",
    "# Run the test\n",
    "test_unit_tensor_properties()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53e41625",
   "metadata": {
    "cell_marker": "\"\"\"",
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   "source": [
    "### 🧪 Unit Test: Tensor Arithmetic Operations\n",
    "\n",
    "This test validates your tensor arithmetic implementation (addition, multiplication, subtraction, division) and operator overloading, ensuring mathematical operations work correctly with proper broadcasting."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "def test_unit_tensor_arithmetic():\n",
    "    \"\"\"Comprehensive test of tensor arithmetic operations.\"\"\"\n",
    "    print(\"🔬 Testing comprehensive tensor arithmetic...\")\n",
    "    \n",
    "    a = Tensor([1, 2, 3])\n",
    "    b = Tensor([4, 5, 6])\n",
    "    \n",
    "    # Test addition\n",
    "    c = a + b\n",
    "    expected = np.array([5, 7, 9])\n",
    "    assert np.array_equal(c.data, expected)\n",
    "    \n",
    "    # Test multiplication\n",
    "    d = a * b\n",
    "    expected = np.array([4, 10, 18])\n",
    "    assert np.array_equal(d.data, expected)\n",
    "\n",
    "    # Test subtraction\n",
    "    e = b - a\n",
    "    expected = np.array([3, 3, 3])\n",
    "    assert np.array_equal(e.data, expected)\n",
    "\n",
    "    # Test division\n",
    "    f = b / a\n",
    "    expected = np.array([4.0, 2.5, 2.0])\n",
    "    assert np.allclose(f.data, expected)\n",
    "    print(\"✅ Tensor arithmetic tests passed!\")\n",
    "\n",
    "# Run the test\n",
    "test_unit_tensor_arithmetic()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0eb9f796",
   "metadata": {},
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   "source": [
    "def test_module_tensor_numpy_integration():\n",
    "    \"\"\"\n",
    "    Integration test for tensor operations with NumPy arrays.\n",
    "    \n",
    "    Tests that tensors properly integrate with NumPy operations and maintain\n",
    "    compatibility with the scientific Python ecosystem.\n",
    "    \"\"\"\n",
    "    print(\"🔬 Running Integration Test: Tensor-NumPy Integration...\")\n",
    "    \n",
    "    # Test 1: Tensor from NumPy array\n",
    "    numpy_array = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "    tensor_from_numpy = Tensor(numpy_array)\n",
    "    \n",
    "    assert tensor_from_numpy.shape == (2, 3), \"Tensor should preserve NumPy array shape\"\n",
    "    assert np.array_equal(tensor_from_numpy.data, numpy_array), \"Tensor should preserve NumPy array data\"\n",
    "    \n",
    "    # Test 2: Tensor arithmetic with NumPy-compatible operations\n",
    "    a = Tensor([1.0, 2.0, 3.0])\n",
    "    b = Tensor([4.0, 5.0, 6.0])\n",
    "    \n",
    "    # Test operations that would be used in neural networks\n",
    "    dot_product_result = np.dot(a.data, b.data)  # Common in layers\n",
    "    assert np.isclose(dot_product_result, 32.0), \"Dot product should work with tensor data\"\n",
    "    \n",
    "    # Test 3: Broadcasting compatibility\n",
    "    matrix = Tensor([[1, 2], [3, 4]])\n",
    "    scalar = Tensor(10)\n",
    "    \n",
    "    result = matrix + scalar\n",
    "    expected = np.array([[11, 12], [13, 14]])\n",
    "    assert np.array_equal(result.data, expected), \"Broadcasting should work like NumPy\"\n",
    "    \n",
    "    # Test 4: Integration with scientific computing patterns\n",
    "    data = Tensor([1, 4, 9, 16, 25])\n",
    "    sqrt_result = Tensor(np.sqrt(data.data))  # Using NumPy functions on tensor data\n",
    "    expected_sqrt = np.array([1., 2., 3., 4., 5.])\n",
    "    assert np.allclose(sqrt_result.data, expected_sqrt), \"Should integrate with NumPy functions\"\n",
    "    \n",
    "    print(\"✅ Integration Test Passed: Tensor-NumPy integration works correctly.\")\n",
    "\n",
    "# Run the integration test\n",
    "test_module_tensor_numpy_integration()"
   ]
  },
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   "source": [
    "## 🎯 MODULE SUMMARY: Tensor Foundation\n",
    "\n",
    "Congratulations! You've successfully implemented the fundamental data structure that powers all machine learning:\n",
    "\n",
    "### ✅ What You've Built\n",
    "- **Tensor Class**: N-dimensional array wrapper with professional interfaces\n",
    "- **Core Operations**: Creation, property access, and arithmetic operations\n",
    "- **Shape Management**: Automatic shape tracking and validation\n",
    "- **Data Types**: Proper NumPy integration and type handling\n",
    "- **Foundation**: The building block for all subsequent TinyTorch modules\n",
    "\n",
    "### ✅ Key Learning Outcomes\n",
    "- **Understanding**: How tensors work as the foundation of machine learning\n",
    "- **Implementation**: Built tensor operations from scratch\n",
    "- **Professional patterns**: Clean APIs, proper error handling, comprehensive testing\n",
    "- **Real-world connection**: Understanding PyTorch/TensorFlow tensor foundations\n",
    "- **Systems thinking**: Building reliable, reusable components\n",
    "\n",
    "### ✅ Mathematical Foundations Mastered\n",
    "- **N-dimensional arrays**: Shape, size, and dimensionality concepts\n",
    "- **Element-wise operations**: Addition, subtraction, multiplication, division\n",
    "- **Broadcasting**: Understanding how operations work with different shapes\n",
    "- **Memory management**: Efficient data storage and access patterns\n",
    "\n",
    "### ✅ Professional Skills Developed\n",
    "- **API design**: Clean, intuitive interfaces for tensor operations\n",
    "- **Error handling**: Graceful handling of invalid operations and edge cases\n",
    "- **Testing methodology**: Comprehensive validation of tensor functionality\n",
    "- **Documentation**: Clear, educational documentation with examples\n",
    "\n",
    "### ✅ Ready for Advanced Applications\n",
    "Your tensor implementation now enables:\n",
    "- **Neural Networks**: Foundation for all layer implementations\n",
    "- **Automatic Differentiation**: Gradient computation through computational graphs\n",
    "- **Complex Models**: CNNs, RNNs, Transformers - all built on tensors\n",
    "- **Real Applications**: Training models on real datasets\n",
    "\n",
    "### 🔗 Connection to Real ML Systems\n",
    "Your implementation mirrors production systems:\n",
    "- **PyTorch**: `torch.Tensor` provides identical functionality\n",
    "- **TensorFlow**: `tf.Tensor` implements similar concepts\n",
    "- **NumPy**: `numpy.ndarray` serves as the foundation\n",
    "- **Industry Standard**: Every major ML framework uses these exact principles\n",
    "\n",
    "### 🎯 The Power of Tensors\n",
    "You've built the fundamental data structure of modern AI:\n",
    "- **Universality**: Tensors represent all data: images, text, audio, video\n",
    "- **Efficiency**: Vectorized operations enable fast computation\n",
    "- **Scalability**: Handles everything from single numbers to massive matrices\n",
    "- **Flexibility**: Foundation for any mathematical operation\n",
    "\n",
    "### 🚀 What's Next\n",
    "Your tensor implementation is the foundation for:\n",
    "- **Activations**: Nonlinear functions that enable complex learning\n",
    "- **Layers**: Linear transformations and neural network building blocks\n",
    "- **Networks**: Composing layers into powerful architectures\n",
    "- **Training**: Optimizing networks to solve real problems\n",
    "\n",
    "**Next Module**: Activation functions - adding the nonlinearity that makes neural networks powerful!\n",
    "\n",
    "You've built the foundation of modern AI. Now let's add the mathematical functions that enable machines to learn complex patterns!"
   ]
  }
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