standalone/ndarray: improve {reshape,broadcast_to,transpose} tests
Print their shapes and exhaustively print all contents.
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@ -204,29 +204,65 @@ def test_ndarray_transpose():
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y = np_transpose(x)
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z = np_transpose(y)
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output_int32(np_shape(x)[0])
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output_int32(np_shape(x)[1])
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output_ndarray_float_2(x)
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output_int32(np_shape(y)[0])
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output_int32(np_shape(y)[1])
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output_ndarray_float_2(y)
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output_int32(np_shape(z)[0])
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output_int32(np_shape(z)[1])
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output_ndarray_float_2(z)
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def test_ndarray_reshape():
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w: ndarray[float, 1] = np_array([1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])
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x = np_reshape(w, (1, 2, 1, -1))
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y = np_reshape(x, [2, -1])
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z = np_reshape(y, 10)
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output_int32(np_shape(w)[0])
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output_ndarray_float_1(w)
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output_int32(np_shape(x)[0])
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output_int32(np_shape(x)[1])
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output_int32(np_shape(x)[2])
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output_int32(np_shape(x)[3])
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output_ndarray_float_4(x)
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output_int32(np_shape(y)[0])
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output_int32(np_shape(y)[1])
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output_ndarray_float_2(y)
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output_int32(np_shape(z)[0])
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output_ndarray_float_1(z)
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x1: ndarray[int32, 1] = np_array([1, 2, 3, 4])
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x2: ndarray[int32, 2] = np_reshape(x1, (2, 2))
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output_ndarray_float_1(w)
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output_ndarray_float_2(y)
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output_ndarray_float_1(z)
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output_int32(np_shape(x1)[0])
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output_ndarray_int32_1(x1)
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output_int32(np_shape(x2)[0])
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output_int32(np_shape(x2)[1])
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output_ndarray_int32_2(x2)
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def test_ndarray_broadcast_to():
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xs = np_array([1.0, 2.0, 3.0])
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ys = np_broadcast_to(xs, (1, 3))
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zs = np_broadcast_to(ys, (2, 4, 3))
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output_int32(np_shape(xs)[0])
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output_ndarray_float_1(xs)
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output_int32(np_shape(ys)[0])
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output_int32(np_shape(ys)[1])
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output_ndarray_float_2(ys)
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output_int32(np_shape(zs)[0])
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output_int32(np_shape(zs)[1])
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output_int32(np_shape(zs)[2])
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output_ndarray_float_3(zs)
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def test_ndarray_subscript_assignment():
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