Fix CS1570 (XML comment has badly formed XML)

Author: sharwell

Directory.Build.props

@@ -12,13 +12,6 @@
     -->
     <GenerateDocumentationFile>true</GenerateDocumentationFile>
     <NoWarn>$(NoWarn),1573,1591,1712</NoWarn>
-
-    <!--
-      Suppress warnings for currently-invalid documentation comments.
-
-      CS1570: XML comment has badly formed XML
-    -->
-    <NoWarn>$(NoWarn),1570</NoWarn>
   </PropertyGroup>
 
 </Project>

src/TensorFlowNET.Core/APIs/tf.array.cs

@@ -48,7 +48,7 @@ public Tensor batch_to_space_nd<T>(T input, int[] block_shape, int[,] crops, str
         /// <typeparam name="T1"></typeparam>
         /// <typeparam name="T2"></typeparam>
         /// <param name="tensor">N-D tensor.</param>
-        /// <param name="mask">K-D boolean tensor, K <= N and K must be known statically.</param>
+        /// <param name="mask">K-D boolean tensor, K &lt;= N and K must be known statically.</param>
         /// <param name="name"></param>
         /// <param name="axis">A 0-D int Tensor representing the axis in tensor to mask from. </param>
         /// <returns>(N-K+1)-dimensional tensor populated by entries in tensor corresponding to True values in mask.</returns>

src/TensorFlowNET.Core/APIs/tf.math.cs

@@ -170,7 +170,7 @@ public Tensor greater_equal<Tx, Ty>(Tx x, Ty y, string name = null)
             => gen_math_ops.greater_equal(x, y, name);
 
         /// <summary>
-        /// Returns the truth value of (x < y) element-wise.
+        /// Returns the truth value of (x &lt; y) element-wise.
         /// </summary>
         /// <typeparam name="Tx"></typeparam>
         /// <typeparam name="Ty"></typeparam>
@@ -191,7 +191,7 @@ public Tensor lgamma(Tensor x, string name = null)
             => gen_math_ops.lgamma(x, name: name);
 
         /// <summary>
-        /// Returns the truth value of (x <= y) element-wise.
+        /// Returns the truth value of (x &lt;= y) element-wise.
         /// </summary>
         /// <typeparam name="Tx"></typeparam>
         /// <typeparam name="Ty"></typeparam>
@@ -344,7 +344,7 @@ public Tensor maximum<T1, T2>(T1 x, T2 y, string name = null)
             => gen_math_ops.maximum(x, y, name: name);
 
         /// <summary>
-        /// Returns the min of x and y (i.e. x < y ? x : y) element-wise.
+        /// Returns the min of x and y (i.e. x &lt; y ? x : y) element-wise.
         /// </summary>
         /// <typeparam name="T1"></typeparam>
         /// <typeparam name="T2"></typeparam>

src/TensorFlowNET.Core/Eager/c_api.eager.cs

@@ -231,7 +231,7 @@ public delegate void delete_backward_function_callback(string op_name,
         /// <summary>
         /// 
         /// </summary>
-        /// <param name="t">const tensorflow::Tensor&</param>
+        /// <param name="t">const tensorflow::Tensor&amp;</param>
         /// <returns>TFE_TensorHandle*</returns>
         [DllImport(TensorFlowLibName)]
         public static extern TFE_TensorHandle TFE_NewTensorHandle(IntPtr t, SafeStatusHandle status);

src/TensorFlowNET.Core/Gradients/math_grad.cs

@@ -393,7 +393,7 @@ private static Tensor[] _MinOrMaxGrad(Operation op, Tensor[] grads)
         }
 
         /// <summary>
-        /// Returns grad*(x > y, x <= y) with type of grad.
+        /// Returns grad*(x > y, x &lt;= y) with type of grad.
         /// </summary>
         /// <param name="op"></param>
         /// <param name="grads"></param>
@@ -405,7 +405,7 @@ public static Tensor[] _MaximumGrad(Operation op, Tensor[] grads)
         }
 
         /// <summary>
-        /// Returns grad*(x < y, x >= y) with type of grad.
+        /// Returns grad*(x &lt; y, x >= y) with type of grad.
         /// </summary>
         /// <param name="op"></param>
         /// <param name="grads"></param>

src/TensorFlowNET.Core/Operations/gen_logging_ops.cs

@@ -45,7 +45,7 @@ public static Tensor histogram_summary(string tag, Tensor values, string name =
         ///    Tags for the summary.
         /// </param>
         /// <param name="values">
-        ///    Same shape as <c>tags.  Values for the summary.
+        ///    Same shape as <c>tags</c>.  Values for the summary.
         /// </param>
         /// <param name="name">
         /// If specified, the created operation in the graph will be this one, otherwise it will be named 'ScalarSummary'.

src/TensorFlowNET.Core/Operations/gen_ops.cs

@@ -6154,11 +6154,11 @@ public static Tensor crop_and_resize (Tensor image, Tensor boxes, Tensor box_ind
         ///    in normalized coordinates <c>[y1, x1, y2, x2]</c>. A normalized coordinate value of
         ///    <c>y</c> is mapped to the image coordinate at <c>y * (image_height - 1)</c>, so as the
         ///    <c>[0, 1]</c> interval of normalized image height is mapped to
-        ///    <c>[0, image_height - 1] in image height coordinates. We do allow y1 &amp;gt; y2, in
+        ///    <c>[0, image_height - 1]</c> in image height coordinates. We do allow y1 &amp;gt; y2, in
         ///    which case the sampled crop is an up-down flipped version of the original
         ///    image. The width dimension is treated similarly. Normalized coordinates
-        ///    outside the </c>[0, 1]<c> range are allowed, in which case we use
-        ///    </c>extrapolation_value<c> to extrapolate the input image values.
+        ///    outside the <c>[0, 1]</c> range are allowed, in which case we use
+        ///    <c>extrapolation_value</c> to extrapolate the input image values.
         /// </param>
         /// <param name="box_ind">
         ///    A 1-D tensor of shape <c>[num_boxes]</c> with int32 values in <c>[0, batch)</c>.
@@ -6200,11 +6200,11 @@ public static Tensor crop_and_resize_grad_boxes (Tensor grads, Tensor image, Ten
         ///    in normalized coordinates <c>[y1, x1, y2, x2]</c>. A normalized coordinate value of
         ///    <c>y</c> is mapped to the image coordinate at <c>y * (image_height - 1)</c>, so as the
         ///    <c>[0, 1]</c> interval of normalized image height is mapped to
-        ///    <c>[0, image_height - 1] in image height coordinates. We do allow y1 &amp;gt; y2, in
+        ///    <c>[0, image_height - 1]</c> in image height coordinates. We do allow y1 &amp;gt; y2, in
         ///    which case the sampled crop is an up-down flipped version of the original
         ///    image. The width dimension is treated similarly. Normalized coordinates
-        ///    outside the </c>[0, 1]<c> range are allowed, in which case we use
-        ///    </c>extrapolation_value<c> to extrapolate the input image values.
+        ///    outside the <c>[0, 1]</c> range are allowed, in which case we use
+        ///    <c>extrapolation_value</c> to extrapolate the input image values.
         /// </param>
         /// <param name="box_ind">
         ///    A 1-D tensor of shape <c>[num_boxes]</c> with int32 values in <c>[0, batch)</c>.
@@ -15982,9 +15982,9 @@ public static Tensor matrix_determinant (Tensor input, string name = "MatrixDete
         ///    everything else padded with zeros. The diagonal is computed as follows:
         ///    
         ///    Assume <c>diagonal</c> has <c>k</c> dimensions <c>[I, J, K, ..., N]</c>, then the output is a
-        ///    tensor of rank <c>k+1</c> with dimensions [I, J, K, ..., N, N]<c> where:
+        ///    tensor of rank <c>k+1</c> with dimensions <c>[I, J, K, ..., N, N]</c> where:
         ///    
-        ///    </c>output[i, j, k, ..., m, n] = 1{m=n} * diagonal[i, j, k, ..., n]<c>.
+        ///    <c>output[i, j, k, ..., m, n] = 1{m=n} * diagonal[i, j, k, ..., n]</c>.
         ///    
         ///    For example:
         ///    
@@ -18540,7 +18540,8 @@ public static Tensor nth_element (Tensor input, Tensor n, bool? reverse = null,
         ///    ][
         ///    [0.0, 1.0, 0.0]  // one_hot(1)
         ///    [0.0, 0.0, 0.0]  // one_hot(-1)
-        ///    ]<c></c><c>
+        ///    ]
+        ///   </code>
         /// </remarks>
         public static Tensor one_hot (Tensor indices, Tensor depth, Tensor on_value, Tensor off_value, int? axis = null, string name = "OneHot")
         {
@@ -21850,7 +21851,6 @@ public static (Tensor activations, Tensor min_activations, Tensor max_activation
         ///    The Operation can be fetched from any of the Tensorreturned in the tuple values, by fetching the Operation property.
         /// </returns>
         /// <remarks>
-        ///   <code>
         /// </remarks>
         public static (Tensor output, Tensor output_min, Tensor output_max) quantized_reshape (Tensor tensor, Tensor shape, Tensor input_min, Tensor input_max, string name = "QuantizedReshape")
         {
@@ -26970,10 +26970,10 @@ public static Operation resource_sparse_apply_r_m_s_prop (Tensor var, Tensor ms,
         /// <remarks>
         ///    The values of <c>value</c> are assigned to the positions in the variable
         ///    <c>ref</c> that are selected by the slice parameters. The slice parameters
-        ///    <c>begin, </c>end<c>, </c>strides<c>, etc. work exactly as in </c>StridedSlice<c>.
+        ///    <c>begin</c>, <c>end</c>, <c>strides</c>, etc. work exactly as in <c>StridedSlice</c>.
         ///    
-        ///    NOTE this op currently does not support broadcasting and so </c>value<c>'s
-        ///    shape must be exactly the shape produced by the slice of </c>ref<c>.
+        ///    NOTE this op currently does not support broadcasting and so <c>value</c>'s
+        ///    shape must be exactly the shape produced by the slice of <c>ref</c>.
         /// </remarks>
         public static Operation resource_strided_slice_assign (Tensor referecne, Tensor begin, Tensor end, Tensor strides, Tensor value, int? begin_mask = null, int? end_mask = null, int? ellipsis_mask = null, int? new_axis_mask = null, int? shrink_axis_mask = null, string name = "ResourceStridedSliceAssign")
         {
@@ -28068,7 +28068,7 @@ public static Operation save_v2 (Tensor prefix, Tensor tensor_names, Tensor shap
         ///    Tags for the summary.
         /// </param>
         /// <param name="values">
-        ///    Same shape as <c>tags.  Values for the summary.
+        ///    Same shape as <c>tags</c>.  Values for the summary.
         /// </param>
         /// <param name="name">
         /// If specified, the created operation in the graph will be this one, otherwise it will be named 'ScalarSummary'.
@@ -34548,10 +34548,10 @@ public static Tensor strided_slice (Tensor input, Tensor begin, Tensor end, Tens
         /// <remarks>
         ///    The values of <c>value</c> are assigned to the positions in the variable
         ///    <c>ref</c> that are selected by the slice parameters. The slice parameters
-        ///    <c>begin, </c>end<c>, </c>strides<c>, etc. work exactly as in </c>StridedSlice<c>.
+        ///    <c>begin</c>, <c>end</c>, <c>strides</c>, etc. work exactly as in <c>StridedSlice</c>.
         ///    
-        ///    NOTE this op currently does not support broadcasting and so </c>value<c>'s
-        ///    shape must be exactly the shape produced by the slice of </c>ref<c>.
+        ///    NOTE this op currently does not support broadcasting and so <c>value</c>'s
+        ///    shape must be exactly the shape produced by the slice of <c>ref</c>.
         /// </remarks>
         public static Tensor strided_slice_assign (Tensor referecne, Tensor begin, Tensor end, Tensor strides, Tensor value, int? begin_mask = null, int? end_mask = null, int? ellipsis_mask = null, int? new_axis_mask = null, int? shrink_axis_mask = null, string name = "StridedSliceAssign")
         {
@@ -36554,21 +36554,21 @@ public static Tensor tensor_array_split_v2 (Tensor handle, Tensor value, Tensor
         ///    and that <c>value</c> has shape
         ///    
         ///   <code>
-        ///    (n0 + n1 + ... + n(T-1) x d0 x d1 x ...)<c></c><c>,
+        ///    (n0 + n1 + ... + n(T-1) x d0 x d1 x ...)</code>,
         ///    
         ///    this splits values into a TensorArray with T tensors.
         ///    
         ///    TensorArray index t will be the subtensor of values with starting position
         ///    
-        ///   </code>
+        ///   <code>
         ///    (n0 + n1 + ... + n(t-1), 0, 0, ...)
-        ///    <code>
+        ///    </code>
         ///    
         ///    and having size
         ///    
-        ///   </code>
+        ///   <code>
         ///    nt x d0 x d1 x ...
-        ///    <code>
+        ///    </code>
         /// </remarks>
         public static Tensor tensor_array_split_v3 (Tensor handle, Tensor value, Tensor lengths, Tensor flow_in, string name = "TensorArraySplitV3")
         {
@@ -38107,9 +38107,9 @@ public static (Tensor y, Tensor idx) unique (Tensor x, TF_DataType? out_idx = nu
         ///    This operation also returns a tensor <c>idx</c> that is the same size as
         ///    the number of the elements in <c>x</c> along the <c>axis</c> dimension. It
         ///    contains the index in the unique output <c>y</c>.
-        ///    In other words, for an <c>1-D</c> tensor <c>x</c> with <c>axis = None:
+        ///    In other words, for an <c>1-D</c> tensor <c>x</c> with <c>axis = None</c>:
         ///    
-        ///    </c>y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]<c>
+        ///    <c>y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]</c>
         ///    
         ///    For example:
         ///    
@@ -38120,7 +38120,7 @@ public static (Tensor y, Tensor idx) unique (Tensor x, TF_DataType? out_idx = nu
         ///    idx ==&amp;gt; [0, 0, 1, 2, 2, 2, 3, 4, 4]
         ///   </code>
         ///    
-        ///    For an </c>2-D<c> tensor </c>x<c> with </c>axis = 0<c>:
+        ///    For an <c>2-D</c> tensor <c>x</c> with <c>axis = 0</c>:
         ///    
         ///   <code>
         ///    # tensor 'x' is [[1, 0, 0],
@@ -38132,7 +38132,7 @@ public static (Tensor y, Tensor idx) unique (Tensor x, TF_DataType? out_idx = nu
         ///    idx ==&amp;gt; [0, 0, 1]
         ///   </code>
         ///    
-        ///    For an </c>2-D<c> tensor </c>x<c> with </c>axis = 1<c>:
+        ///    For an <c>2-D</c> tensor <c>x</c> with <c>axis = 1</c>:
         ///    
         ///   <code>
         ///    # tensor 'x' is [[1, 0, 0],
@@ -38241,9 +38241,9 @@ public static (Tensor y, Tensor idx, Tensor count) unique_with_counts (Tensor x,
         ///    that are the same size as the number of the elements in <c>x</c> along the
         ///    <c>axis</c> dimension. The <c>idx</c> contains the index in the unique output <c>y</c>
         ///    and the <c>count</c> contains the count in the unique output <c>y</c>.
-        ///    In other words, for an <c>1-D</c> tensor <c>x</c> with <c>axis = None:
+        ///    In other words, for an <c>1-D</c> tensor <c>x</c> with <c>axis = None</c>:
         ///    
-        ///    </c>y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]<c>
+        ///    <c>y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]</c>
         ///    
         ///    For example:
         ///    
@@ -38255,7 +38255,7 @@ public static (Tensor y, Tensor idx, Tensor count) unique_with_counts (Tensor x,
         ///    count ==&amp;gt; [2, 1, 3, 1, 2]
         ///   </code>
         ///    
-        ///    For an </c>2-D<c> tensor </c>x<c> with </c>axis = 0<c>:
+        ///    For an <c>2-D</c> tensor <c>x</c> with <c>axis = 0</c>:
         ///    
         ///   <code>
         ///    # tensor 'x' is [[1, 0, 0],
@@ -38268,7 +38268,7 @@ public static (Tensor y, Tensor idx, Tensor count) unique_with_counts (Tensor x,
         ///    count ==&amp;gt; [2, 1]
         ///   </code>
         ///    
-        ///    For an </c>2-D<c> tensor </c>x<c> with </c>axis = 1<c>:
+        ///    For an <c>2-D</c> tensor <c>x</c> with <c>axis = 1</c>:
         ///    
         ///   <code>
         ///    # tensor 'x' is [[1, 0, 0],

src/TensorFlowNET.Core/Operations/math_ops.cs

@@ -406,10 +406,10 @@ public static Tensor realdiv(Tensor x, Tensor y, string name = null)
         /// Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each
         /// entry in `axis`. If `keepdims` is true, the reduced dimensions
         /// are retained with length 1.
-
+        ///
         /// If `axis` has no entries, all dimensions are reduced, and a
         /// tensor with a single element is returned.
-
+        ///
         /// This function is more numerically stable than log(sum(exp(input))). It avoids
         /// overflows caused by taking the exp of large inputs and underflows caused by
         /// taking the log of small inputs.

src/TensorFlowNET.Core/Status/c_api.status.cs

@@ -54,7 +54,7 @@ public partial class c_api
         public static extern SafeStatusHandle TF_NewStatus();
 
         /// <summary>
-        /// Record <code, msg> in *s.  Any previous information is lost.
+        /// Record &lt;code, msg> in *s.  Any previous information is lost.
         /// A common use is to clear a status: TF_SetStatus(s, TF_OK, "");
         /// </summary>
         /// <param name="s"></param>

src/TensorFlowNET.Core/Summaries/Summary.cs

@@ -74,7 +74,7 @@ public Tensor scalar(string name, Tensor tensor, string[] collections = null, st
         /// <summary>
         /// Adds keys to a collection.
         /// </summary>
-        /// <param name="val"The value to add per each key.></param>
+        /// <param name="val">The value to add per each key.</param>
         /// <param name="collections">A collection of keys to add.</param>
         /// <param name="default_collections">Used if collections is None.</param>
         public void collect(ITensorOrOperation val, List<string> collections, List<string> default_collections)

src/TensorFlowNET.Core/Tensors/c_api.tensor.cs

@@ -56,7 +56,7 @@ public partial class c_api
 
         /// <summary>
         /// Return the length of the tensor in the "dim_index" dimension.
-        /// REQUIRES: 0 <= dim_index < TF_NumDims(tensor)
+        /// REQUIRES: 0 &lt;= dim_index &lt; TF_NumDims(tensor)
         /// </summary>
         /// <param name="tensor"></param>
         /// <param name="dim_index"></param>

src/TensorFlowNET.Core/ops.cs

@@ -124,7 +124,7 @@ public static Tensor internal_convert_to_tensor_or_composite(Tensor value, TF_Da
         /// Wrapper for `Graph.control_dependencies()` using the default graph.
         /// 
         /// See `tf.Graph.control_dependencies` for more details.
-
+        ///
         /// When eager execution is enabled, any callable object in the `control_inputs`
         /// list will be called.
         /// </summary>