add more tensor stuff.

lib/src/splat/model.dart

@@ -11,38 +11,38 @@ Tensor randomQuantTensor(int n) {
   Tensor v = Tensor.random([n]);
   Tensor w = Tensor.random([n]);
 
-  Tensor a1 = u.each((f, _, __) {
+  Tensor a1 = u.each((d) {
-    return sin(2 * pi * f);
+    return sin(2 * pi * d);
   });
-  Tensor a2 = u.each((f, _, __) {
+  Tensor a2 = u.each((d) {
-    return sqrt(1 - f);
+    return sqrt(1 - d);
   });
   Tensor a = a1 * a2;
 
-  Tensor b1 = v.each((f, _, __) {
+  Tensor b1 = v.each((d) {
-    return cos(2 * pi * f);
+    return cos(2 * pi * d);
   });
-  Tensor b2 = v.each((f, _, __) {
+  Tensor b2 = v.each((d) {
-    return sqrt(1 - f);
+    return sqrt(1 - d);
   });
 
   Tensor b = b1 * b2;
 
-  Tensor c1 = u.each((f, _, __) {
+  Tensor c1 = u.each((d) {
-    return sqrt(f);
+    return sqrt(d);
   });
-  Tensor c2 = w.each((f, _, __) {
+  Tensor c2 = w.each((d) {
-    return sin(2 * pi * f);
+    return sin(2 * pi * d);
   });
   Tensor c = c1 * c2;
 
-  Tensor d1 = u.each((f, _, __) {
+  Tensor d1 = u.each((d) {
-    return sqrt(f);
+    return sqrt(d);
   });
-  Tensor d2 = w.each((f, _, __) {
+  Tensor d2 = w.each((d) {
-    return cos(2 * pi * f);
+    return cos(2 * pi * d);
   });
   Tensor d = d1 * d2;
 
-  return Tensor.stack(Tensor([a[0], b[0], c[0], d[0]]));
+  return Tensor.stack([a, b, c, d]);
 }

lib/src/splat/tensor.dart

@@ -84,6 +84,50 @@ class Tensor {
     return data.equals(other.data);
   }
 
+// Static method 'stack' that takes a list of Tensors and optionally an integer `dim` as arguments,
+// implementing pytorch's stack method.
+  static Tensor stack(List<Tensor> tensors, [int dim = 0]) {
+    // If the provided dimension is less than 0 or greater than the number of dimensions in all Tensors, use 0 as the default value for dim.
+    dim = (dim < 0 || tensors.any((tensor) => tensor.shape.length <= dim))
+        ? 0
+        : dim;
+
+    List<dynamic> newShape = [
+      ...List<dynamic>.filled(tensors.length, 1),
+      ...tensors[0].shape
+    ];
+
+    // If the provided dimension is not the first dimension of all Tensors, adjust the shape accordingly.
+    if (dim > 0) {
+      for (var i = 0; i < tensors.length; i++) {
+        newShape[i] *= tensors[i].shape[dim];
+      }
+    }
+
+    // If the provided dimension is not the first dimension of all Tensors, adjust the shape accordingly.
+    if (dim > 0) {
+      for (var i = 0; i < tensors.length; i++) {
+        newShape[i] *= tensors[i].shape[dim];
+      }
+    }
+
+    List<dynamic> stackedData = [];
+
+    // Iterate through the data of each tensor and concatenate it to the stackedData list.
+    for (var i = 0; i < newShape[0]; i++) {
+      int currentIndex = 0;
+      for (var j = 0; j < tensors.length; j++) {
+        if (i >= currentIndex * tensors[j].shape[dim] &&
+            i < (currentIndex + 1) * tensors[j].shape[dim]) {
+          stackedData.add(tensors[j].data[currentIndex]);
+          currentIndex++;
+        }
+      }
+    }
+
+    return Tensor.fromList(stackedData);
+  }
+
   operator *(Tensor other) {
     return Tensor.fromList(listBroadcast(data, other.data, (d1, d2) {
       return d1 * d2;

lib/src/test/splat/tensor_test.dart

@@ -76,4 +76,25 @@ void main() {
     ]);
     expect((t1 * t2).data, equals(expected.data));
   });
+
+  test("tensors can be stacked", () {
+    List<List<double>> l = [
+      [0.3367, 0.1288, 02345],
+      [0.2303, -1.1229, -0.1863]
+    ];
+    Tensor baseTensor = Tensor.fromList(l);
+    Tensor stacked_1 = Tensor.stack([baseTensor, baseTensor]);
+    expect(
+        stacked_1.data,
+        equals([
+          [
+            [0.3367, 0.1288, 0.2345],
+            [0.2303, -1.1229, -0.1863]
+          ],
+          [
+            [0.3367, 0.1288, 0.2345],
+            [0.2303, -1.1229, -0.1863]
+          ]
+        ]));
+  });
 }