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TensorShape: Added implicit conversions for object type.
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‎src/TensorFlowNET.Core/Tensors/TensorShape.cs‎

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@@ -254,6 +254,22 @@ public override string ToString()
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public static explicit operator (int, int, int, int, int, int)(TensorShape shape) => shape.dims.Length == 6 ? (shape.dims[0], shape.dims[1], shape.dims[2], shape.dims[3], shape.dims[4], shape.dims[5]) : (0, 0, 0, 0, 0, 0);
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public static implicit operator TensorShape((int, int, int, int, int, int) dims) => new TensorShape(dims.Item1, dims.Item2, dims.Item3, dims.Item4, dims.Item5, dims.Item6);
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public static explicit operator (int, int, int, int, int, int, int)(TensorShape shape) => shape.dims.Length == 7 ? (shape.dims[0], shape.dims[1], shape.dims[2], shape.dims[3], shape.dims[4], shape.dims[5], shape.dims[6]) : (0, 0, 0, 0, 0, 0, 0);
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public static implicit operator TensorShape((int, int, int, int, int, int, int) dims) => new TensorShape(dims.Item1, dims.Item2, dims.Item3, dims.Item4, dims.Item5, dims.Item6, dims.Item7);
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public static explicit operator (int, int, int, int, int, int, int, int)(TensorShape shape) => shape.dims.Length == 8 ? (shape.dims[0], shape.dims[1], shape.dims[2], shape.dims[3], shape.dims[4], shape.dims[5], shape.dims[6], shape.dims[7]) : (0, 0, 0, 0, 0, 0, 0, 0);
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public static implicit operator TensorShape((int, int, int, int, int, int, int, int) dims) => new TensorShape(dims.Item1, dims.Item2, dims.Item3, dims.Item4, dims.Item5, dims.Item6, dims.Item7, dims.Item8);
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public static implicit operator TensorShape(int?[] dims) => new TensorShape(dims);
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public static implicit operator TensorShape(int? dim) => new TensorShape(dim);
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public static implicit operator TensorShape((object, object) dims) => new TensorShape(dims.Item1, dims.Item2);
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public static implicit operator TensorShape((object, object, object) dims) => new TensorShape(dims.Item1, dims.Item2, dims.Item3);
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public static implicit operator TensorShape((object, object, object, object) dims) => new TensorShape(dims.Item1, dims.Item2, dims.Item3, dims.Item4);
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public static implicit operator TensorShape((object, object, object, object, object) dims) => new TensorShape(dims.Item1, dims.Item2, dims.Item3, dims.Item4, dims.Item5);
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public static implicit operator TensorShape((object, object, object, object, object, object) dims) => new TensorShape(dims.Item1, dims.Item2, dims.Item3, dims.Item4, dims.Item5, dims.Item6);
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public static implicit operator TensorShape((object, object, object, object, object, object, object) dims) => new TensorShape(dims.Item1, dims.Item2, dims.Item3, dims.Item4, dims.Item5, dims.Item6, dims.Item7);
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public static implicit operator TensorShape((object, object, object, object, object, object, object, object) dims) => new TensorShape(dims.Item1, dims.Item2, dims.Item3, dims.Item4, dims.Item5, dims.Item6, dims.Item7, dims.Item8);
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}
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}

‎test/TensorFlowNET.UnitTest/TensorShapeTest.cs‎

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using System;
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using Microsoft.VisualStudio.TestTools.UnitTesting;
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using NumSharp;
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using Tensorflow;
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using static Tensorflow.Binding;
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namespace TensorFlowNET.UnitTest
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{
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[TestClass]
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public class TensorShapeTest
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{
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[TestMethod]
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public void Case1()
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{
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int? a = 2;
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int? b = 3;
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var dims = new object[] {(int?) None, a, b};
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new TensorShape(dims).GetPrivate<Shape>("shape").Should().BeShaped(-1, 2, 3);
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}
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[TestMethod]
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public void Case2()
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{
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int? a = 2;
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int? b = 3;
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var dims = new object[] {(int?) None, a, b};
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new TensorShape(new object[] {dims}).GetPrivate<Shape>("shape").Should().BeShaped(-1, 2, 3);
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}
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[TestMethod]
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public void Case3()
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{
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int? a = 2;
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int? b = null;
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var dims = new object[] {(int?) None, a, b};
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new TensorShape(new object[] {dims}).GetPrivate<Shape>("shape").Should().BeShaped(-1, 2, -1);
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}
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[TestMethod]
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public void Case4()
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{
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TensorShape shape = (None, None);
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shape.GetPrivate<Shape>("shape").Should().BeShaped(-1, -1);
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}
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[TestMethod]
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public void Case5()
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{
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TensorShape shape = (1, None, 3);
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shape.GetPrivate<Shape>("shape").Should().BeShaped(1, -1, 3);
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}
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[TestMethod]
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public void Case6()
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{
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TensorShape shape = (None, 1, 2, 3, None);
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shape.GetPrivate<Shape>("shape").Should().BeShaped(-1, 1, 2, 3, -1);
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}
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}
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}

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