content: /*
   * Tencent is pleased to support the open source community by making Angel available.
   *
   * Copyright (C) 2017-2018 THL A29 Limited, a Tencent company. All rights reserved.
   *
   * Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in
   * compliance with the License. You may obtain a copy of the License at
   *
   * https://opensource.org/licenses/Apache-2.0
   *
   * Unless required by applicable law or agreed to in writing, software distributed under the License
   * is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
   * or implied. See the License for the specific language governing permissions and limitations under
   * the License.
   *
   */
  // Generated by the protocol buffer compiler.  DO NOT EDIT!
  // source: apis/common/tensor_shape.proto
  
  package com.tencent.angel.serving.apis.common;
  
  public final class TensorShapeProtos {
    private TensorShapeProtos() {}
    public static void registerAllExtensions(
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    public static void registerAllExtensions(
        com.google.protobuf.ExtensionRegistry registry) {
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    public interface TensorShapeProtoOrBuilder extends
        // @@protoc_insertion_point(interface_extends:angel.TensorShapeProto)
        com.google.protobuf.MessageOrBuilder {
  
      /**
       * <pre>
       * Dimensions of the tensor, such as {"input", 30}, {"output", 40}
       * for a 30 x 40 2D tensor.  If an entry has size -1, this
       * corresponds to a dimension of unknown size. The names are
       * optional.
       * The order of entries in "dim" matters: It indicates the layout of the
       * values in the tensor in-memory representation.
       * The first entry in "dim" is the outermost dimension used to layout the
       * values, the last entry is the innermost dimension.  This matches the
       * in-memory layout of RowMajor Eigen tensors.
       * If "dim.size()" &gt; 0, "unknown_rank" must be false.
       * </pre>
       *
       * <code>repeated .angel.TensorShapeProto.Dim dim = 2;</code>
       */
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          getDimList();
      /**
       * <pre>
       * Dimensions of the tensor, such as {"input", 30}, {"output", 40}
       * for a 30 x 40 2D tensor.  If an entry has size -1, this
       * corresponds to a dimension of unknown size. The names are
       * optional.
       * The order of entries in "dim" matters: It indicates the layout of the
       * values in the tensor in-memory representation.
       * The first entry in "dim" is the outermost dimension used to layout the
       * values, the last entry is the innermost dimension.  This matches the
       * in-memory layout of RowMajor Eigen tensors.
       * If "dim.size()" &gt; 0, "unknown_rank" must be false.
       * </pre>
       *
       * <code>repeated .angel.TensorShapeProto.Dim dim = 2;</code>
       */
      com.tencent.angel.serving.apis.common.TensorShapeProtos.TensorShapeProto.Dim getDim(int index);
      /**
       * <pre>
       * Dimensions of the tensor, such as {"input", 30}, {"output", 40}
       * for a 30 x 40 2D tensor.  If an entry has size -1, this
       * corresponds to a dimension of unknown size. The names are
       * optional.
       * The order of entries in "dim" matters: It indicates the layout of the
       * values in the tensor in-memory representation.
       * The first entry in "dim" is the outermost dimension used to layout the
       * values, the last entry is the innermost dimension.  This matches the
       * in-memory layout of RowMajor Eigen tensors.
       * If "dim.size()" &gt; 0, "unknown_rank" must be false.
       * </pre>
       *
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       */
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       * <pre>
       * Dimensions of the tensor, such as {"input", 30}, {"output", 40}
       * for a 30 x 40 2D tensor.  If an entry has size -1, this
       * corresponds to a dimension of unknown size. The names are
       * optional.
       * The order of entries in "dim" matters: It indicates the layout of the
       * values in the tensor in-memory representation.
       * The first entry in "dim" is the outermost dimension used to layout the
       * values, the last entry is the innermost dimension.  This matches the
       * in-memory layout of RowMajor Eigen tensors.
       * If "dim.size()" &gt; 0, "unknown_rank" must be false.
       * </pre>
       *
       * <code>repeated .angel.TensorShapeProto.Dim dim = 2;</code>
       */
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      /**
       * <pre>
       * Dimensions of the tensor, such as {"input", 30}, {"output", 40}
       * for a 30 x 40 2D tensor.  If an entry has size -1, this
       * corresponds to a dimension of unknown size. The names are
       * optional.
       * The order of entries in "dim" matters: It indicates the layout of the
       * values in the tensor in-memory representation.
       * The first entry in "dim" is the outermost dimension used to layout the
       * values, the last entry is the innermost dimension.  This matches the
       * in-memory layout of RowMajor Eigen tensors.
       * If "dim.size()" &gt; 0, "unknown_rank" must be false.
       * </pre>
       *
       * <code>repeated .angel.TensorShapeProto.Dim dim = 2;</code>
       */
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          int index);
  
      /**
       * <pre>
       * If true, the number of dimensions in the shape is unknown.
       * If true, "dim.size()" must be 0.
       * </pre>
       *
       * <code>bool unknown_rank = 3;</code>
       */
      boolean getUnknownRank();
    }
    /**
     * <pre>
     * Dimensions of a tensor.
     * </pre>
     *
     * Protobuf type {@code angel.TensorShapeProto}
     */
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       * <pre>
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       * </pre>
       *
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            return java.util.Collections.unmodifiableList(dim_);
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            return dimBuilder_.getMessageList();
          }
        }
        /**
         * <pre>
         * Dimensions of the tensor, such as {"input", 30}, {"output", 40}
         * for a 30 x 40 2D tensor.  If an entry has size -1, this
         * corresponds to a dimension of unknown size. The names are
         * optional.
         * The order of entries in "dim" matters: It indicates the layout of the
         * values in the tensor in-memory representation.
         * The first entry in "dim" is the outermost dimension used to layout the
         * values, the last entry is the innermost dimension.  This matches the
         * in-memory layout of RowMajor Eigen tensors.
         * If "dim.size()" &gt; 0, "unknown_rank" must be false.
         * </pre>
         *
         * <code>repeated .angel.TensorShapeProto.Dim dim = 2;</code>
         */
        public int getDimCount() {
          if (dimBuilder_ == null) {
            return dim_.size();
          } else {
            return dimBuilder_.getCount();
          }
        }
        /**
         * <pre>
         * Dimensions of the tensor, such as {"input", 30}, {"output", 40}
         * for a 30 x 40 2D tensor.  If an entry has size -1, this
         * corresponds to a dimension of unknown size. The names are
         * optional.
         * The order of entries in "dim" matters: It indicates the layout of the
         * values in the tensor in-memory representation.
         * The first entry in "dim" is the outermost dimension used to layout the
         * values, the last entry is the innermost dimension.  This matches the
         * in-memory layout of RowMajor Eigen tensors.
         * If "dim.size()" &gt; 0, "unknown_rank" must be false.
         * </pre>
         *
         * <code>repeated .angel.TensorShapeProto.Dim dim = 2;</code>
         */
        public com.tencent.angel.serving.apis.common.TensorShapeProtos.TensorShapeProto.Dim getDim(int index) {
          if (dimBuilder_ == null) {
            return dim_.get(index);
          } else {
            return dimBuilder_.getMessage(index);
          }
        }
        /**
         * <pre>
         * Dimensions of the tensor, such as {"input", 30}, {"output", 40}
         * for a 30 x 40 2D tensor.  If an entry has size -1, this
         * corresponds to a dimension of unknown size. The names are
         * optional.
         * The order of entries in "dim" matters: It indicates the layout of the
         * values in the tensor in-memory representation.
         * The first entry in "dim" is the outermost dimension used to layout the
         * values, the last entry is the innermost dimension.  This matches the
         * in-memory layout of RowMajor Eigen tensors.
         * If "dim.size()" &gt; 0, "unknown_rank" must be false.
         * </pre>
         *
         * <code>repeated .angel.TensorShapeProto.Dim dim = 2;</code>
         */
        public Builder setDim(
            int index, com.tencent.angel.serving.apis.common.TensorShapeProtos.TensorShapeProto.Dim value) {
          if (dimBuilder_ == null) {
            if (value == null) {
              throw new NullPointerException();
            }
            ensureDimIsMutable();
            dim_.set(index, value);
            onChanged();
          } else {
            dimBuilder_.setMessage(index, value);
          }
          return this;
        }
        /**
         * <pre>
         * Dimensions of the tensor, such as {"input", 30}, {"output", 40}
         * for a 30 x 40 2D tensor.  If an entry has size -1, this
         * corresponds to a dimension of unknown size. The names are
         * optional.
         * The order of entries in "dim" matters: It indicates the layout of the
         * values in the tensor in-memory representation.
         * The first entry in "dim" is the outermost dimension used to layout the
         * values, the last entry is the innermost dimension.  This matches the
         * in-memory layout of RowMajor Eigen tensors.
         * If "dim.size()" &gt; 0, "unknown_rank" must be false.
         * </pre>
         *
         * <code>repeated .angel.TensorShapeProto.Dim dim = 2;</code>
         */
        public Builder setDim(
            int index, com.tencent.angel.serving.apis.common.TensorShapeProtos.TensorShapeProto.Dim.Builder builderForValue) {
          if (dimBuilder_ == null) {
            ensureDimIsMutable();
            dim_.set(index, builderForValue.build());
            onChanged();
          } else {
            dimBuilder_.setMessage(index, builderForValue.build());
          }
          return this;
        }
        /**
         * <pre>
         * Dimensions of the tensor, such as {"input", 30}, {"output", 40}
         * for a 30 x 40 2D tensor.  If an entry has size -1, this
         * corresponds to a dimension of unknown size. The names are
         * optional.
         * The order of entries in "dim" matters: It indicates the layout of the
         * values in the tensor in-memory representation.
         * The first entry in "dim" is the outermost dimension used to layout the
         * values, the last entry is the innermost dimension.  This matches the
         * in-memory layout of RowMajor Eigen tensors.
         * If "dim.size()" &gt; 0, "unknown_rank" must be false.
         * </pre>
         *
         * <code>repeated .angel.TensorShapeProto.Dim dim = 2;</code>
         */
        public Builder addDim(com.tencent.angel.serving.apis.common.TensorShapeProtos.TensorShapeProto.Dim value) {
          if (dimBuilder_ == null) {
            if (value == null) {
              throw new NullPointerException();
            }
            ensureDimIsMutable();
            dim_.add(value);
            onChanged();
          } else {
            dimBuilder_.addMessage(value);
          }
          return this;
        }
        /**
         * <pre>
         * Dimensions of the tensor, such as {"input", 30}, {"output", 40}
         * for a 30 x 40 2D tensor.  If an entry has size -1, this
         * corresponds to a dimension of unknown size. The names are
         * optional.
         * The order of entries in "dim" matters: It indicates the layout of the
         * values in the tensor in-memory representation.
         * The first entry in "dim" is the outermost dimension used to layout the
         * values, the last entry is the innermost dimension.  This matches the
         * in-memory layout of RowMajor Eigen tensors.
         * If "dim.size()" &gt; 0, "unknown_rank" must be false.
         * </pre>
         *
         * <code>repeated .angel.TensorShapeProto.Dim dim = 2;</code>
         */
        public Builder addDim(
            int index, com.tencent.angel.serving.apis.common.TensorShapeProtos.TensorShapeProto.Dim value) {
          if (dimBuilder_ == null) {
            if (value == null) {
              throw new NullPointerException();
            }
            ensureDimIsMutable();
            dim_.add(index, value);
            onChanged();
          } else {
            dimBuilder_.addMessage(index, value);
          }
          return this;
        }
        /**
         * <pre>
         * Dimensions of the tensor, such as {"input", 30}, {"output", 40}
         * for a 30 x 40 2D tensor.  If an entry has size -1, this
         * corresponds to a dimension of unknown size. The names are
         * optional.
         * The order of entries in "dim" matters: It indicates the layout of the
         * values in the tensor in-memory representation.
         * The first entry in "dim" is the outermost dimension used to layout the
         * values, the last entry is the innermost dimension.  This matches the
         * in-memory layout of RowMajor Eigen tensors.
         * If "dim.size()" &gt; 0, "unknown_rank" must be false.
         * </pre>
         *
         * <code>repeated .angel.TensorShapeProto.Dim dim = 2;</code>
         */
        public Builder addDim(
            com.tencent.angel.serving.apis.common.TensorShapeProtos.TensorShapeProto.Dim.Builder builderForValue) {
          if (dimBuilder_ == null) {
            ensureDimIsMutable();
            dim_.add(builderForValue.build());
            onChanged();
          } else {
            dimBuilder_.addMessage(builderForValue.build());
          }
          return this;
        }
        /**
         * <pre>
         * Dimensions of the tensor, such as {"input", 30}, {"output", 40}
         * for a 30 x 40 2D tensor.  If an entry has size -1, this
         * corresponds to a dimension of unknown size. The names are
         * optional.
         * The order of entries in "dim" matters: It indicates the layout of the
         * values in the tensor in-memory representation.
         * The first entry in "dim" is the outermost dimension used to layout the
         * values, the last entry is the innermost dimension.  This matches the
         * in-memory layout of RowMajor Eigen tensors.
         * If "dim.size()" &gt; 0, "unknown_rank" must be false.
         * </pre>
         *
         * <code>repeated .angel.TensorShapeProto.Dim dim = 2;</code>
         */
        public Builder addDim(
            int index, com.tencent.angel.serving.apis.common.TensorShapeProtos.TensorShapeProto.Dim.Builder builderForValue) {
          if (dimBuilder_ == null) {
            ensureDimIsMutable();
            dim_.add(index, builderForValue.build());
            onChanged();
          } else {
            dimBuilder_.addMessage(index, builderForValue.build());
          }
          return this;
        }
        /**
         * <pre>
         * Dimensions of the tensor, such as {"input", 30}, {"output", 40}
         * for a 30 x 40 2D tensor.  If an entry has size -1, this
         * corresponds to a dimension of unknown size. The names are
         * optional.
         * The order of entries in "dim" matters: It indicates the layout of the
         * values in the tensor in-memory representation.
         * The first entry in "dim" is the outermost dimension used to layout the
         * values, the last entry is the innermost dimension.  This matches the
         * in-memory layout of RowMajor Eigen tensors.
         * If "dim.size()" &gt; 0, "unknown_rank" must be false.
         * </pre>
         *
         * <code>repeated .angel.TensorShapeProto.Dim dim = 2;</code>
         */
        public Builder addAllDim(
            java.lang.Iterable<? extends com.tencent.angel.serving.apis.common.TensorShapeProtos.TensorShapeProto.Dim> values) {
          if (dimBuilder_ == null) {
            ensureDimIsMutable();
            com.google.protobuf.AbstractMessageLite.Builder.addAll(
                values, dim_);
            onChanged();
          } else {
            dimBuilder_.addAllMessages(values);
          }
          return this;
        }
        /**
         * <pre>
         * Dimensions of the tensor, such as {"input", 30}, {"output", 40}
         * for a 30 x 40 2D tensor.  If an entry has size -1, this
         * corresponds to a dimension of unknown size. The names are
         * optional.
         * The order of entries in "dim" matters: It indicates the layout of the
         * values in the tensor in-memory representation.
         * The first entry in "dim" is the outermost dimension used to layout the
         * values, the last entry is the innermost dimension.  This matches the
         * in-memory layout of RowMajor Eigen tensors.
         * If "dim.size()" &gt; 0, "unknown_rank" must be false.
         * </pre>
         *
         * <code>repeated .angel.TensorShapeProto.Dim dim = 2;</code>
         */
        public Builder clearDim() {
          if (dimBuilder_ == null) {
            dim_ = java.util.Collections.emptyList();
            bitField0_ = (bitField0_ & ~0x00000001);
            onChanged();
          } else {
            dimBuilder_.clear();
          }
          return this;
        }
        /**
         * <pre>
         * Dimensions of the tensor, such as {"input", 30}, {"output", 40}
         * for a 30 x 40 2D tensor.  If an entry has size -1, this
         * corresponds to a dimension of unknown size. The names are
         * optional.
         * The order of entries in "dim" matters: It indicates the layout of the
         * values in the tensor in-memory representation.
         * The first entry in "dim" is the outermost dimension used to layout the
         * values, the last entry is the innermost dimension.  This matches the
         * in-memory layout of RowMajor Eigen tensors.
         * If "dim.size()" &gt; 0, "unknown_rank" must be false.
         * </pre>
         *
         * <code>repeated .angel.TensorShapeProto.Dim dim = 2;</code>
         */
        public Builder removeDim(int index) {
          if (dimBuilder_ == null) {
            ensureDimIsMutable();
            dim_.remove(index);
            onChanged();
          } else {
            dimBuilder_.remove(index);
          }
          return this;
        }
        /**
         * <pre>
         * Dimensions of the tensor, such as {"input", 30}, {"output", 40}
         * for a 30 x 40 2D tensor.  If an entry has size -1, this
         * corresponds to a dimension of unknown size. The names are
         * optional.
         * The order of entries in "dim" matters: It indicates the layout of the
         * values in the tensor in-memory representation.
         * The first entry in "dim" is the outermost dimension used to layout the
         * values, the last entry is the innermost dimension.  This matches the
         * in-memory layout of RowMajor Eigen tensors.
         * If "dim.size()" &gt; 0, "unknown_rank" must be false.
         * </pre>
         *
         * <code>repeated .angel.TensorShapeProto.Dim dim = 2;</code>
         */
        public com.tencent.angel.serving.apis.common.TensorShapeProtos.TensorShapeProto.Dim.Builder getDimBuilder(
            int index) {
          return getDimFieldBuilder().getBuilder(index);
        }
        /**
         * <pre>
         * Dimensions of the tensor, such as {"input", 30}, {"output", 40}
         * for a 30 x 40 2D tensor.  If an entry has size -1, this
         * corresponds to a dimension of unknown size. The names are
         * optional.
         * The order of entries in "dim" matters: It indicates the layout of the
         * values in the tensor in-memory representation.
         * The first entry in "dim" is the outermost dimension used to layout the
         * values, the last entry is the innermost dimension.  This matches the
         * in-memory layout of RowMajor Eigen tensors.
         * If "dim.size()" &gt; 0, "unknown_rank" must be false.
         * </pre>
         *
         * <code>repeated .angel.TensorShapeProto.Dim dim = 2;</code>
         */
        public com.tencent.angel.serving.apis.common.TensorShapeProtos.TensorShapeProto.DimOrBuilder getDimOrBuilder(
            int index) {
          if (dimBuilder_ == null) {
            return dim_.get(index);  } else {
            return dimBuilder_.getMessageOrBuilder(index);
          }
        }
        /**
         * <pre>
         * Dimensions of the tensor, such as {"input", 30}, {"output", 40}
         * for a 30 x 40 2D tensor.  If an entry has size -1, this
         * corresponds to a dimension of unknown size. The names are
         * optional.
         * The order of entries in "dim" matters: It indicates the layout of the
         * values in the tensor in-memory representation.
         * The first entry in "dim" is the outermost dimension used to layout the
         * values, the last entry is the innermost dimension.  This matches the
         * in-memory layout of RowMajor Eigen tensors.
         * If "dim.size()" &gt; 0, "unknown_rank" must be false.
         * </pre>
         *
         * <code>repeated .angel.TensorShapeProto.Dim dim = 2;</code>
         */
        public java.util.List<? extends com.tencent.angel.serving.apis.common.TensorShapeProtos.TensorShapeProto.DimOrBuilder> 
             getDimOrBuilderList() {
          if (dimBuilder_ != null) {
            return dimBuilder_.getMessageOrBuilderList();
          } else {
            return java.util.Collections.unmodifiableList(dim_);
          }
        }
        /**
         * <pre>
         * Dimensions of the tensor, such as {"input", 30}, {"output", 40}
         * for a 30 x 40 2D tensor.  If an entry has size -1, this
         * corresponds to a dimension of unknown size. The names are
         * optional.
         * The order of entries in "dim" matters: It indicates the layout of the
         * values in the tensor in-memory representation.
         * The first entry in "dim" is the outermost dimension used to layout the
         * values, the last entry is the innermost dimension.  This matches the
         * in-memory layout of RowMajor Eigen tensors.
         * If "dim.size()" &gt; 0, "unknown_rank" must be false.
         * </pre>
         *
         * <code>repeated .angel.TensorShapeProto.Dim dim = 2;</code>
         */
        public com.tencent.angel.serving.apis.common.TensorShapeProtos.TensorShapeProto.Dim.Builder addDimBuilder() {
          return getDimFieldBuilder().addBuilder(
              com.tencent.angel.serving.apis.common.TensorShapeProtos.TensorShapeProto.Dim.getDefaultInstance());
        }
        /**
         * <pre>
         * Dimensions of the tensor, such as {"input", 30}, {"output", 40}
         * for a 30 x 40 2D tensor.  If an entry has size -1, this
         * corresponds to a dimension of unknown size. The names are
         * optional.
         * The order of entries in "dim" matters: It indicates the layout of the
         * values in the tensor in-memory representation.
         * The first entry in "dim" is the outermost dimension used to layout the
         * values, the last entry is the innermost dimension.  This matches the
         * in-memory layout of RowMajor Eigen tensors.
         * If "dim.size()" &gt; 0, "unknown_rank" must be false.
         * </pre>
         *
         * <code>repeated .angel.TensorShapeProto.Dim dim = 2;</code>
         */
        public com.tencent.angel.serving.apis.common.TensorShapeProtos.TensorShapeProto.Dim.Builder addDimBuilder(
            int index) {
          return getDimFieldBuilder().addBuilder(
              index, com.tencent.angel.serving.apis.common.TensorShapeProtos.TensorShapeProto.Dim.getDefaultInstance());
        }
        /**
         * <pre>
         * Dimensions of the tensor, such as {"input", 30}, {"output", 40}
         * for a 30 x 40 2D tensor.  If an entry has size -1, this
         * corresponds to a dimension of unknown size. The names are
         * optional.
         * The order of entries in "dim" matters: It indicates the layout of the
         * values in the tensor in-memory representation.
         * The first entry in "dim" is the outermost dimension used to layout the
         * values, the last entry is the innermost dimension.  This matches the
         * in-memory layout of RowMajor Eigen tensors.
         * If "dim.size()" &gt; 0, "unknown_rank" must be false.
         * </pre>
         *
         * <code>repeated .angel.TensorShapeProto.Dim dim = 2;</code>
         */
        public java.util.List<com.tencent.angel.serving.apis.common.TensorShapeProtos.TensorShapeProto.Dim.Builder> 
             getDimBuilderList() {
          return getDimFieldBuilder().getBuilderList();
        }
        private com.google.protobuf.RepeatedFieldBuilderV3<
            com.tencent.angel.serving.apis.common.TensorShapeProtos.TensorShapeProto.Dim, com.tencent.angel.serving.apis.common.TensorShapeProtos.TensorShapeProto.Dim.Builder, com.tencent.angel.serving.apis.common.TensorShapeProtos.TensorShapeProto.DimOrBuilder> 
            getDimFieldBuilder() {
          if (dimBuilder_ == null) {
            dimBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3<
                com.tencent.angel.serving.apis.common.TensorShapeProtos.TensorShapeProto.Dim, com.tencent.angel.serving.apis.common.TensorShapeProtos.TensorShapeProto.Dim.Builder, com.tencent.angel.serving.apis.common.TensorShapeProtos.TensorShapeProto.DimOrBuilder>(
                    dim_,
                    ((bitField0_ & 0x00000001) == 0x00000001),
                    getParentForChildren(),
                    isClean());
            dim_ = null;
          }
          return dimBuilder_;
        }
  
        private boolean unknownRank_ ;
        /**
         * <pre>
         * If true, the number of dimensions in the shape is unknown.
         * If true, "dim.size()" must be 0.
         * </pre>
         *
         * <code>bool unknown_rank = 3;</code>
         */
        public boolean getUnknownRank() {
          return unknownRank_;
        }
        /**
         * <pre>
         * If true, the number of dimensions in the shape is unknown.
         * If true, "dim.size()" must be 0.
         * </pre>
         *
         * <code>bool unknown_rank = 3;</code>
         */
        public Builder setUnknownRank(boolean value) {
          
          unknownRank_ = value;
          onChanged();
          return this;
        }
        /**
         * <pre>
         * If true, the number of dimensions in the shape is unknown.
         * If true, "dim.size()" must be 0.
         * </pre>
         *
         * <code>bool unknown_rank = 3;</code>
         */
        public Builder clearUnknownRank() {
          
          unknownRank_ = false;
          onChanged();
          return this;
        }
        public final Builder setUnknownFields(
            final com.google.protobuf.UnknownFieldSet unknownFields) {
          return super.setUnknownFieldsProto3(unknownFields);
        }
  
        public final Builder mergeUnknownFields(
            final com.google.protobuf.UnknownFieldSet unknownFields) {
          return super.mergeUnknownFields(unknownFields);
        }
  
  
        // @@protoc_insertion_point(builder_scope:angel.TensorShapeProto)
      }
  
      // @@protoc_insertion_point(class_scope:angel.TensorShapeProto)
      private static final com.tencent.angel.serving.apis.common.TensorShapeProtos.TensorShapeProto DEFAULT_INSTANCE;
      static {
        DEFAULT_INSTANCE = new com.tencent.angel.serving.apis.common.TensorShapeProtos.TensorShapeProto();
      }
  
      public static com.tencent.angel.serving.apis.common.TensorShapeProtos.TensorShapeProto getDefaultInstance() {
        return DEFAULT_INSTANCE;
      }
  
      private static final com.google.protobuf.Parser<TensorShapeProto>
          PARSER = new com.google.protobuf.AbstractParser<TensorShapeProto>() {
        public TensorShapeProto parsePartialFrom(
            com.google.protobuf.CodedInputStream input,
            com.google.protobuf.ExtensionRegistryLite extensionRegistry)
            throws com.google.protobuf.InvalidProtocolBufferException {
          return new TensorShapeProto(input, extensionRegistry);
        }
      };
  
      public static com.google.protobuf.Parser<TensorShapeProto> parser() {
        return PARSER;
      }
  
      @java.lang.Override
      public com.google.protobuf.Parser<TensorShapeProto> getParserForType() {
        return PARSER;
      }
  
      public com.tencent.angel.serving.apis.common.TensorShapeProtos.TensorShapeProto getDefaultInstanceForType() {
        return DEFAULT_INSTANCE;
      }
  
    }
  
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