chore: run v fmt -w . with latest V

autograd/node.v

@@ -21,9 +21,9 @@ pub mut:
 // node
 pub fn node[T](gate Gate, parents []&Variable[T], payload &Payload[T], name string) &Node[T] {
 	return &Node[T]{
-		gate: gate
+		gate:    gate
 		parents: parents
 		payload: payload
-		name: name
+		name:    name
 	}
 }

autograd/variable.v

@@ -39,9 +39,9 @@ pub struct VariableData {
 pub fn variable[T](context &Context[T], value &vtl.Tensor[T], data VariableData) &Variable[T] {
 	grad := if data.requires_grad { vtl.zeros_like[T](value) } else { value }
 	return &Variable[T]{
-		context: context
-		value: value
-		grad: grad
+		context:       context
+		value:         value
+		grad:          grad
 		requires_grad: data.requires_grad
 	}
 }

datasets/imdb.v

@@ -18,11 +18,11 @@ pub:
 // load_imdb_helper loads the IMDB dataset for a given split.
 fn load_imdb_helper(split string) !(&vtl.Tensor[string], &vtl.Tensor[int]) {
 	dataset_path := download_dataset(
-		dataset: 'imdb'
-		baseurl: datasets.imdb_base_url
-		compressed: true
+		dataset:          'imdb'
+		baseurl:          imdb_base_url
+		compressed:       true
 		uncompressed_dir: 'aclImdb'
-		file: datasets.imdb_file_name
+		file:             imdb_file_name
 	)!
 
 	mut split_paths := []string{}
@@ -63,8 +63,8 @@ pub fn load_imdb() !ImdbDataset {
 
 	return ImdbDataset{
 		train_features: train_features
-		train_labels: train_labels
-		test_features: test_features
-		test_labels: test_labels
+		train_labels:   train_labels
+		test_features:  test_features
+		test_labels:    test_labels
 	}
 }

datasets/loader.v

@@ -85,8 +85,8 @@ fn download_dataset(data DatasetDownload) !string {
 			file_content := os.read_file(target)!
 			content := gzip.decompress(file_content.bytes(),
 				verify_header_checksum: true
-				verify_length: false
-				verify_checksum: false
+				verify_length:          false
+				verify_checksum:        false
 			)!
 			os.write_file(uncompressed_path, content.bytestr())!
 		}

datasets/mnist.v

@@ -21,11 +21,11 @@ pub:
 // load_mnist_helper loads the MNIST dataset from the given filename.
 fn load_mnist_helper(file string) !string {
 	dataset_path := download_dataset(
-		dataset: 'mnist'
-		baseurl: datasets.mnist_base_url
-		compressed: true
+		dataset:          'mnist'
+		baseurl:          mnist_base_url
+		compressed:       true
 		uncompressed_dir: file.all_before_last('.')
-		file: file
+		file:             file
 	)!
 
 	return os.read_file(dataset_path)!
@@ -47,15 +47,15 @@ fn load_mnist_labels(filename string) !&vtl.Tensor[u8] {
 
 // load_mnist loads the MNIST dataset.
 pub fn load_mnist() !MnistDataset {
-	train_features := load_mnist_features(datasets.mnist_train_images_file)!
-	train_labels := load_mnist_labels(datasets.mnist_train_labels_file)!
-	test_features := load_mnist_features(datasets.mnist_test_images_file)!
-	test_labels := load_mnist_labels(datasets.mnist_test_labels_file)!
+	train_features := load_mnist_features(mnist_train_images_file)!
+	train_labels := load_mnist_labels(mnist_train_labels_file)!
+	test_features := load_mnist_features(mnist_test_images_file)!
+	test_labels := load_mnist_labels(mnist_test_labels_file)!
 
 	return MnistDataset{
 		train_features: train_features
-		train_labels: train_labels
-		test_features: test_features
-		test_labels: test_labels
+		train_labels:   train_labels
+		test_features:  test_features
+		test_labels:    test_labels
 	}
 }

examples/vtl_plot_scatter_colorscale/main.v

@@ -23,11 +23,11 @@ fn main() {
 
 	mut plt := plot.Plot.new()
 	plt.scatter(
-		x: x.to_array()
-		y: y
-		mode: 'lines+markers'
+		x:          x.to_array()
+		y:          y
+		mode:       'lines+markers'
 		colorscale: 'smoker'
-		marker: plot.Marker{
+		marker:     plot.Marker{
 			size: []f64{len: x.size, init: 10.0}
 		}
 	)

nn/gates/layers/flatten.v

@@ -11,7 +11,7 @@ pub:
 
 pub fn flatten_gate[T](input &autograd.Variable[T], cached_shape []int) &FlattenGate[T] {
 	return &FlattenGate[T]{
-		input: input
+		input:        input
 		cached_shape: cached_shape
 	}
 }

nn/gates/layers/linear.v

@@ -14,9 +14,9 @@ pub:
 
 pub fn linear_gate[T](input &autograd.Variable[T], weight &autograd.Variable[T], bias &autograd.Variable[T]) &LinearGate[T] {
 	return &LinearGate[T]{
-		input: input
+		input:  input
 		weight: weight
-		bias: bias
+		bias:   bias
 	}
 }
 

nn/gates/layers/maxpool.v

@@ -16,10 +16,10 @@ pub:
 pub fn maxpool2d_gate[T](max_indices &vtl.Tensor[int], kernel []int, shape []int, stride []int, padding []int) &MaxPool2DGate[T] {
 	return &MaxPool2DGate[T]{
 		max_indices: max_indices
-		kernel: kernel
-		shape: shape
-		stride: stride
-		padding: padding
+		kernel:      kernel
+		shape:       shape
+		stride:      stride
+		padding:     padding
 	}
 }
 

nn/gates/loss/mse.v

@@ -12,7 +12,7 @@ pub:
 
 pub fn mse_gate[T](cache &autograd.Variable[T], target &vtl.Tensor[T]) &MseGate[T] {
 	return &MseGate[T]{
-		cache: cache
+		cache:  cache
 		target: target
 	}
 }

nn/gates/loss/sigmoid_cross_entropy.v

@@ -12,7 +12,7 @@ pub:
 
 pub fn sigmoid_cross_entropy_gate[T](cache &autograd.Variable[T], target &vtl.Tensor[T]) &SigmoidCrossEntropyGate[T] {
 	return &SigmoidCrossEntropyGate[T]{
-		cache: cache
+		cache:  cache
 		target: target
 	}
 }

nn/gates/loss/softmax_cross_entropy.v

@@ -12,7 +12,7 @@ pub:
 
 pub fn softmax_cross_entropy_gate[T](cache &autograd.Variable[T], target &vtl.Tensor[T]) &SoftmaxCrossEntropyGate[T] {
 	return &SoftmaxCrossEntropyGate[T]{
-		cache: cache
+		cache:  cache
 		target: target
 	}
 }

nn/layers/dropout.v

@@ -20,7 +20,7 @@ pub struct DropoutLayer[T] {
 pub fn dropout_layer[T](ctx &autograd.Context[T], output_shape []int, data DropoutLayerConfig) types.Layer[T] {
 	return types.Layer[T](&DropoutLayer[T]{
 		output_shape: output_shape.clone()
-		prob: 1.0 - data.prob
+		prob:         1.0 - data.prob
 	})
 }
 

nn/layers/elu.v

@@ -20,7 +20,7 @@ pub struct EluLayer[T] {
 pub fn elu_layer[T](ctx &autograd.Context[T], output_shape []int, data EluLayerConfig) types.Layer[T] {
 	return types.Layer[T](&EluLayer[T]{
 		output_shape: output_shape.clone()
-		alpha: data.alpha
+		alpha:        data.alpha
 	})
 }
 

nn/layers/linear.v

@@ -18,7 +18,7 @@ pub fn linear_layer[T](ctx &autograd.Context[T], input_dim int, output_dim int)
 	bias := vtl.zeros[T]([1, output_dim])
 	return types.Layer[T](&LinearLayer[T]{
 		weights: ctx.variable(weights)
-		bias: ctx.variable(bias)
+		bias:    ctx.variable(bias)
 	})
 }
 

nn/layers/maxpool.v

@@ -16,9 +16,9 @@ pub struct MaxPool2DLayer[T] {
 pub fn maxpool2d_layer[T](ctx &autograd.Context[T], input_shape []int, kernel []int, padding []int, stride []int) types.Layer[T] {
 	return types.Layer[T](&MaxPool2DLayer[T]{
 		input_shape: input_shape.clone()
-		kernel: kernel.clone()
-		padding: padding.clone()
-		stride: stride.clone()
+		kernel:      kernel.clone()
+		padding:     padding.clone()
+		stride:      stride.clone()
 	})
 }
 

nn/models/sequential_info.v

@@ -16,9 +16,9 @@ pub mut:
 // with an empty list of layers.
 pub fn sequential_info[T](ctx &autograd.Context[T], layers_ []types.Layer[T]) &SequentialInfo[T] {
 	return &SequentialInfo[T]{
-		ctx: ctx
+		ctx:    ctx
 		layers: layers_
-		loss: unsafe { nil }
+		loss:   unsafe { nil }
 	}
 }
 

nn/optimizers/adam.v

@@ -29,9 +29,9 @@ pub struct AdamOptimizerConfig {
 pub fn adam_optimizer[T](config AdamOptimizerConfig) &AdamOptimizer[T] {
 	return &AdamOptimizer[T]{
 		learning_rate: config.learning_rate
-		beta1: config.beta1
-		beta2: config.beta2
-		epsilon: config.epsilon
+		beta1:         config.beta1
+		beta2:         config.beta2
+		epsilon:       config.epsilon
 	}
 }
 

src/broadcast.v

@@ -80,9 +80,9 @@ pub fn (t &Tensor[T]) broadcast_to[T](shape []int) !&Tensor[T] {
 	strides := strides_from_shape(shape, .row_major)
 	result_strides := broadcast_strides(shape, t.shape, strides, t.strides)!
 	return &Tensor[T]{
-		data: t.data
-		shape: shape
-		size: size
+		data:    t.data
+		shape:   shape
+		size:    size
 		strides: result_strides
 	}
 }

src/broadcast_d_vcl.v

@@ -10,9 +10,9 @@ pub fn (t &VclTensor[T]) broadcast_to[T](shape []int) !&VclTensor[T] {
 	strides := strides_from_shape(shape, .row_major)
 	result_strides := broadcast_strides(shape, t.shape, strides, t.strides)!
 	return &VclTensor[T]{
-		data: t.data
-		shape: shape
-		size: size
+		data:    t.data
+		shape:   shape
+		size:    size
 		strides: result_strides
 	}
 }

src/build.v

@@ -19,20 +19,20 @@ pub fn from_array[T](arr []T, shape []int, params TensorData) !&Tensor[T] {
 	data_storage := storage.from_array[T](arr)
 	if shape.len == 0 {
 		return &Tensor[T]{
-			memory: params.memory
+			memory:  params.memory
 			strides: [1]
-			shape: []
-			size: size
-			data: data_storage
+			shape:   []
+			size:    size
+			data:    data_storage
 		}
 	}
 	strides := strides_from_shape(shape, params.memory)
 	return &Tensor[T]{
-		shape: shape
+		shape:   shape
 		strides: strides
-		memory: params.memory
-		size: size
-		data: data_storage
+		memory:  params.memory
+		size:    size
+		data:    data_storage
 	}
 }
 
@@ -41,22 +41,22 @@ pub fn tensor[T](init T, shape []int, params TensorData) &Tensor[T] {
 	if shape.len == 0 {
 		data_storage := storage.storage[T](1, 0, init)
 		return &Tensor[T]{
-			memory: params.memory
+			memory:  params.memory
 			strides: [1]
-			shape: []
-			size: 1
-			data: data_storage
+			shape:   []
+			size:    1
+			data:    data_storage
 		}
 	}
 	strides := strides_from_shape(shape, params.memory)
 	size := size_from_shape(shape)
 	data_storage := storage.storage[T](size, 0, init)
 	return &Tensor[T]{
-		shape: shape.clone()
-		memory: params.memory
+		shape:   shape.clone()
+		memory:  params.memory
 		strides: strides
-		size: size
-		data: data_storage
+		size:    size
+		data:    data_storage
 	}
 }
 
@@ -65,11 +65,11 @@ pub fn tensor[T](init T, shape []int, params TensorData) &Tensor[T] {
 pub fn tensor_like[T](t &Tensor[T]) &Tensor[T] {
 	data_storage := t.data.like[T]()
 	return &Tensor[T]{
-		shape: t.shape.clone()
+		shape:   t.shape.clone()
 		strides: t.strides.clone()
-		memory: t.memory
-		size: t.size
-		data: data_storage
+		memory:  t.memory
+		size:    t.size
+		data:    data_storage
 	}
 }
 
@@ -80,11 +80,11 @@ pub fn tensor_like_with_shape[T](t &Tensor[T], shape []int) &Tensor[T] {
 	size := size_from_shape(shape)
 	data_storage := t.data.like_with_len[T](size)
 	return &Tensor[T]{
-		shape: shape.clone()
+		shape:   shape.clone()
 		strides: strides
-		memory: t.memory
-		size: size
-		data: data_storage
+		memory:  t.memory
+		size:    size
+		data:    data_storage
 	}
 }
 
@@ -94,10 +94,10 @@ fn tensor_like_with_shape_and_strides[T](t &Tensor[T], shape []int, strides []in
 	size := size_from_shape(shape)
 	data_storage := t.data.like_with_len[T](size)
 	return &Tensor[T]{
-		shape: shape.clone()
+		shape:   shape.clone()
 		strides: strides.clone()
-		memory: t.memory
-		size: size
-		data: data_storage
+		memory:  t.memory
+		size:    size
+		data:    data_storage
 	}
 }