Update eslint.config.js

eslint.config.js

@@ -201,7 +201,7 @@ export default [
             'operator-assignment': 'error',
             'prefer-arrow-callback': 'error',
             'prefer-const': 'error',
-            'prefer-destructuring': ['error', { 'array': true }],
+            'prefer-destructuring': ['error', { 'array': false }],
             // 'prefer-exponentiation-operator': 'error',
             // 'prefer-named-capture-group': 'error',
             'prefer-numeric-literals': 'error',

source/acuity.js

@@ -551,10 +551,8 @@ acuity.Inference = class {
         });
         operators.set('image_resize', (inputs, params) => {
             const newShape = inputs[0].slice();
-            /* eslint-disable prefer-destructuring */
             newShape[1] = params.new_size[0];
             newShape[2] = params.new_size[1];
-            /* eslint-enable prefer-destructuring */
             return [newShape];
         });
         operators.set('argmax', (inputs, params) => {

source/keras.js

@@ -657,9 +657,7 @@ keras.Graph = class {
                             const transform = (value) => {
                                 if (value.every((item) => is_constant(item))) {
                                     for (let i = 0; i < value.length; i++) {
-                                        /* eslint-disable prefer-destructuring */
                                         value[i] = value[i][2];
-                                        /* eslint-enable prefer-destructuring */
                                     }
                                 } else if (value.every((item) => Array.isArray(item))) {
                                     const dims = value.map((item) => transform(item));

source/megengine.js

@@ -488,9 +488,7 @@ megengine.Node = class {
             let qparams = null;
             for (const o of expr.outputs) {
                 if (o._qparams !== null) {
-                    /* eslint-disable prefer-destructuring */
                     qparams = o._qparams[1];
-                    /* eslint-enable prefer-destructuring */
                 }
                 const name = `output${outIdx === 0 ? '' : outIdx}`;
                 const dtype = o._dtype ? o._dtype.__name__ : null;

source/openvino.js

@@ -683,9 +683,7 @@ openvino.Node = class {
                     }
                     case 'Convolution:weights':
                     case 'Deconvolution:weights': {
-                        /* eslint-disable prefer-destructuring */
                         const c = this.inputs[0].value[0].type.shape.dimensions[1];
-                        /* eslint-enable prefer-destructuring */
                         const group = parseInt(layer.data.group || '1', 10);
                         const kernel = layer.data['kernel-x'] !== undefined && layer.data['kernel-y'] !== undefined ?
                             [parseInt(layer.data['kernel-x'], 10), parseInt(layer.data['kernel-y'], 10)] :
@@ -695,9 +693,7 @@ openvino.Node = class {
                         break;
                     }
                     case 'LSTMCell:weights': {
-                        /* eslint-disable prefer-destructuring */
                         const input_size = inputs[0].type.shape.dimensions[1];
-                        /* eslint-enable prefer-destructuring */
                         const hidden_size = parseInt(layer.data.hidden_size, 10);
                         data = weight('W', precision, [4 * hidden_size, input_size], data);
                         data = weight('R', precision, [4 * hidden_size, hidden_size], data);
@@ -709,9 +705,7 @@ openvino.Node = class {
                         break;
                     }
                     case 'GRUCell:weights': {
-                        /* eslint-disable prefer-destructuring */
                         const input_size = inputs[0].type.shape.dimensions[1];
-                        /* eslint-enable prefer-destructuring */
                         const hidden_size = parseInt(layer.data.hidden_size, 10);
                         data = weight('W', precision, [3 * hidden_size, input_size], data);
                         data = weight('R', precision, [3 * hidden_size, hidden_size], data);

source/python.js

@@ -13451,9 +13451,7 @@ python.Execution = class {
                 let lines = null;
                 if (ivalues[0] === 'FORMAT_WITH_STRING_TABLE') {
                     this.deserializer = new torch._C.SourceRangeDeserializer(ivalues[1]);
-                    /* eslint-disable prefer-destructuring */
                     lines = ivalues[2];
-                    /* eslint-enable prefer-destructuring */
                 } else {
                     this.deserializer = new torch._C.SourceRangeDeserializer();
                     lines = ivalues;

source/pytorch.js

@@ -1724,7 +1724,6 @@ pytorch.Execution = class extends python.Execution {
                 const [/* pack_version */, tensors, opt_tensors] = state;
                 const packed_config_tensor = new pytorch.Tensor('', tensors[0], true);
                 const packed_config = packed_config_tensor.decode();
-                /* eslint-disable prefer-destructuring */
                 this.weight = tensors[1];
                 this.bias = opt_tensors[0];
                 this.stride = [packed_config[1], packed_config[2]];
@@ -1732,7 +1731,6 @@ pytorch.Execution = class extends python.Execution {
                 this.dilation = [packed_config[5], packed_config[6]];
                 this.output_padding = [packed_config[7], packed_config[8]];
                 this.groups = packed_config[9];
-                /* eslint-enable prefer-destructuring */
             }
         });
         this.registerType('__torch__.torch.classes.quantized.Conv3dPackedParamsBase', class {
@@ -1743,7 +1741,6 @@ pytorch.Execution = class extends python.Execution {
                 const [/* pack_version */, tensors, opt_tensors] = state;
                 const packed_config_tensor = new pytorch.Tensor('', tensors[0], true);
                 const packed_config = packed_config_tensor.decode();
-                /* eslint-disable prefer-destructuring */
                 this.weight = tensors[1];
                 this.bias = opt_tensors[0];
                 this.stride = [packed_config[1], packed_config[2]];
@@ -1751,7 +1748,6 @@ pytorch.Execution = class extends python.Execution {
                 this.dilation = [packed_config[5], packed_config[6]];
                 this.output_padding = [packed_config[7], packed_config[8]];
                 this.groups = packed_config[9];
-                /* eslint-enable prefer-destructuring */
             }
         });
         this.registerType('__torch__.torch.classes.quantized.LinearPackedParamsBase', class {

source/tengine.js

@@ -526,14 +526,10 @@ tengine.Reader = class {
                 if (node.type === 'Convolution') {
                     switch (subgraph.graphLayout) {
                         case 0: // NCHW
-                            /* eslint-disable prefer-destructuring */
                             node.params[6] = subgraph.tensors[node.inputs[1]].dims[1];
-                            /* eslint-enable prefer-destructuring */
                             break;
                         case 1: // NHWC
-                            /* eslint-disable prefer-destructuring */
                             node.params[6] = subgraph.tensors[node.inputs[1]].dims[3];
-                            /* eslint-enable prefer-destructuring */
                             break;
                         default:
                             throw new tengine.Error(`Unsupported 'Convolution' layout '${subgraph.graphLayout}'.`);