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Title: PyTorch torch.stack Function | Online Tutorial\\n\\nURL Source: https://example.com/pytorch/pytorch-torch-stack.html\\n\\n* * *\\n\\n[ Pytorch torch Reference Manual](https://example.com/pytorch/pytorch-torch-ref.html)\\n\\n`torch.stack` is a function in PyTorch used to stack multiple tensors along a new dimension. It creates a new dimension and places all input tensors along this new dimension.\\n\\nThis is commonly used in deep learning for scenarios such as creating batch data, stacking the outputs of multiple models, etc.\\n\\n### Function Definition\\n\\ntorch.stack(tensors, dim=0, out=None)\\n**Parameters**:\\n\\n* `tensors` (Sequence of Tensor): The sequence of tensors to stack. All tensors must have the same shape.\\n* `dim` (int, optional): The dimension to stack along. Default is 0. The new dimension will be inserted at this position.\\n* `out` (Tensor, optional): The output tensor.\\n\\n**Returns**:\\n\\n* `torch.Tensor`: Returns the stacked tensor.\\n\\n* * *\\n\\n## Usage Examples\\n\\n### Example 1: Basic Stacking\\n\\n## Example\\n\\nimport torch\\n\\n# create two 1D tensors\\n\\n a = torch.tensor([1,2,3])\\n\\n b = torch.tensor([4,5,6])\\n\\n# stack along a new dimension\\n\\n c = torch.stack([a, b])\\n\\nprint("a shape of:", a.shape)\\n\\nprint("b shape of:", b.shape)\\n\\nprint("c shape of:", c.shape)\\n\\nprint(c)\\n\\nThe output is:\\n\\nShape of a: torch.Size() Shape of b: torch.Size() Shape of c: torch.Size([2, 3]) tensor([[1, 2, 3], [4, 5, 6]])\\n### Example 2: Specifying the Stacking Dimension\\n\\n## Example\\n\\nimport torch\\n\\na = torch.tensor([1,2,3])\\n\\n b = torch.tensor([4,5,6])\\n\\n# Along dim=1 stack\\n\\n c = torch.stack([a, b], dim=1)\\n\\nprint("c shape of:", c.shape)\\n\\nprint(c)\\n\\nThe output is:\\n\\nShape of c: torch.Size([3, 2]) tensor([[1, 4], [2, 5], [3, 6]])\\n### Example 3: Stacking Multiple Tensors\\n\\n## Example\\n\\nimport torch\\n\\n# Create multiple tensors\\n\\n tensors =[torch.tensor([i, i+1, i+2])for i in range(5)]\\n\\n# stackall tensors\\n\\n result = torch.stack(tensors)\\n\\nprint("Result Shape:", result.shape)\\n\\nprint(result)\\n\\nThe output is:\\n\\nResult shape: torch.Size([5, 3]) tensor([[0, 1, 2], [1, 2, 3], [2, 3, 4], [3, 4, 5], [4, 5, 6]])\\n### Example 4: Saving Multiple States in a Neural Network\\n\\n## Example\\n\\nimport torch\\n\\n# simulate saving loss values of multiple epochs\\n\\n losses =[]\\n\\nfor epoch in range(5):\\n\\n loss = torch.tensor([epoch * 0.1,(epoch + 1) * 0.1])\\n\\n losses.append(loss)\\n\\n# stackloss for all epochs\\n\\n all_losses = torch.stack(losses)\\n\\nprint("Each epoch loss shape:", all_losses.shape)\\n\\nprint(all_losses)\\n\\nThe output is:\\n\\nShape of losses for each epoch: torch.Size([5, 2]) tensor([[0.0000, 0.1000], [0.1000, 0.2000], [0.2000, 0.3000], [0.3000, 0.4000], [0.4000, 0.5000]])\\n\\n* * *\\n\\n## Difference between torch.stack and torch.cat\\n\\n## Example\\n\\nimport torch\\n\\na = torch.randn(2,3)\\n\\n b = torch.randn(2,3)\\n\\n# stack creates a new dimension\\n\\n stack_result = torch.stack([a, b])\\n\\nprint("stack Result Shape:", stack_result.shape)\\n\\n# cat does not create a new dimension\\n\\n cat_result = torch.cat([a, b], dim=0)\\n\\nprint("cat Result Shape:", cat_result.shape)\\n\\nThe output is:\\n\\nstack result shape: torch.Size([2, 2, 3]) cat result shape: torch.Size([4, 3])\\n* **`torch.stack`**: Stacks along a new dimension. The shapes of the input tensors must be exactly the same, and it adds a new dimension.\\n* **`torch.cat`**: Concatenates along an existing dimension. The input tensors can have different sizes in the concatenation dimension, and it does not add a new dimension.\\n\\n* * *\\n\\n[ Pytorch torch Reference Manual](https://example.com/pytorch/pytorch-torch-ref.html)