text: /* ******************************************************************************
   *
   *
   * This program and the accompanying materials are made available under the
   * terms of the Apache License, Version 2.0 which is available at
   * https://www.apache.org/licenses/LICENSE-2.0.
   *
   *  See the NOTICE file distributed with this work for additional
   *  information regarding copyright ownership.
   * 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.
   *
   * SPDX-License-Identifier: Apache-2.0
   ******************************************************************************/
  
  //
  //  @author raver119@gmail.com
  //
  
  #include <system/op_boilerplate.h>
  #if NOT_EXCLUDED(OP_norm)
  
  #include <ops/declarable/CustomOperations.h>
  #include <ops/declarable/helpers/axis.h>
  
  namespace sd {
      namespace ops {
          REDUCTION_OP_IMPL(norm, 1, 1, false, 1, -2) {
              auto input = INPUT_VARIABLE(0);
              NDArray *output = OUTPUT_VARIABLE(0);
  
              auto mode = (int) T_ARG(0);
              std::vector<int> dims = *block.getIArguments();
              bool overwrite = false;
  
              if (block.width() == 1) {
                  output = OUTPUT_VARIABLE(0);
              } else {
                  auto axisVector = INPUT_VARIABLE(1);
                  dims.resize(axisVector->lengthOf());
                  helpers::adjustAxis(input->rankOf(), axisVector, dims);
                  axisVector->printIndexedBuffer("AXIS");
                  auto shape = ShapeUtils::evalReduceShapeInfo(input->ordering(), dims, *input, false, false);
                  if (!shape::equalsStrict(shape, output->shapeInfo())) {
                      output = new NDArray(shape, false, block.launchContext());
                      overwrite = true;
                  }
              }
              output->printShapeInfo("Output Shape Info");
              switch(mode) {
                  case 0: {
                      REQUIRE_TRUE(dims.size() == 2 || (input->rankOf() == 2 && dims.size() == 0), 0, "Norm: Frobenius is defined for 2D matrices or TADS only");
                      // fro
                      input->reduceAlongDimension(reduce::NormFrobenius, *output, dims, false, output->rankOf() == 2);
                  }
                  break;
                  case 1: {
                      // euclidean
                      if ((input->rankOf() == 2 && dims.size() == 0) || dims.size() == 2) {
                          input->reduceAlongDimension(reduce::NormFrobenius, *output, dims, false, output->rankOf() == 2);
                      } else {
                          input->reduceAlongDimension(reduce::Norm2, *output, dims, false, output->rankOf() == 2);
                      }
                  }
                  break;
                  case 2: {
                      // 1
                      input->reduceAlongDimension(reduce::Norm1, *output, dims, false, output->rankOf() == 2);
                  }
                  break;
                  case 3: {
                      // 2
                      input->reduceAlongDimension(reduce::Norm2, *output, dims, false, output->rankOf() == 2);
                  }
                  break;
                  case 4: {
                      // inf-norm
                      input->reduceAlongDimension(reduce::NormMax, *output, dims, false, output->rankOf() == 2);
                  }
                  break;
                  default: {
                      // p-norm
                      REQUIRE_TRUE(block.getIArguments()->size() > 1, 0, "P-Norm reductions requires 2 TArguments, but only 1 was provided");
                      // FIXME: p is required here
                      //T p = T_ARG(1);
                      input->reduceAlongDimension(reduce::NormP, *output, dims, false, output->rankOf() == 2);
                  }
              }
  
              if (overwrite) {
                  OVERWRITE_RESULT(output);
              }
  
              return ND4J_STATUS_OK;
          };
  
          DECLARE_TYPES(norm) {
              getOpDescriptor()
                      ->setAllowedInputTypes(sd::DataType::ANY)
                      ->setAllowedOutputTypes({ALL_FLOATS});
          }
      }
  }
  
  #endif