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nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt

Summary

Maintainability
F
1 wk
Test Coverage

File TensorflowRuleDeclarations.kt has 311 lines of code (exceeds 250 allowed). Consider refactoring.
Open

/*
 *  ******************************************************************************
 *  *
 *  *
 *  * This program and the accompanying materials are made available under the

    Method convertNDArrayToTensorflowTensor has 82 lines of code (exceeds 25 allowed). Consider refactoring.
    Open

    fun convertNDArrayToTensorflowTensor(arrayToConvert: INDArray): TensorProto {
        if(arrayToConvert.data() == null)
            return TensorProto.getDefaultInstance()
        when(arrayToConvert.dataType()) {
    
    

      Method convertNDArrayToTensorflowTensor has a Cognitive Complexity of 25 (exceeds 20 allowed). Consider refactoring.
      Open

      fun convertNDArrayToTensorflowTensor(arrayToConvert: INDArray): TensorProto {
          if(arrayToConvert.data() == null)
              return TensorProto.getDefaultInstance()
          when(arrayToConvert.dataType()) {
      
      

      Cognitive Complexity

      Cognitive Complexity is a measure of how difficult a unit of code is to intuitively understand. Unlike Cyclomatic Complexity, which determines how difficult your code will be to test, Cognitive Complexity tells you how difficult your code will be to read and comprehend.

      A method's cognitive complexity is based on a few simple rules:

      • Code is not considered more complex when it uses shorthand that the language provides for collapsing multiple statements into one
      • Code is considered more complex for each "break in the linear flow of the code"
      • Code is considered more complex when "flow breaking structures are nested"

      Further reading

      Method conditionalFieldValueIntIndexNDArrayRule has 26 lines of code (exceeds 25 allowed). Consider refactoring.
      Open

      fun conditionalFieldValueIntIndexNDArrayRule(outputAttribute: String,
                                                   inputFrameworkStringNameToTest: String,
                                                   targetValue: String,
                                                   trueIndex: Int,
                                                   falseIndex: Int,

        Method conditionalFieldValueIntIndexArrayRule has 26 lines of code (exceeds 25 allowed). Consider refactoring.
        Open

        fun conditionalFieldValueIntIndexArrayRule(outputAttribute: String,
                                                   inputFrameworkStringNameToTest: String,
                                                   targetValue: String,
                                                   trueIndex: Int,
                                                   falseIndex: Int,

          Method conditionalFieldValueIntIndexNDArrayRule has 7 arguments (exceeds 4 allowed). Consider refactoring.
          Open

          fun conditionalFieldValueIntIndexNDArrayRule(outputAttribute: String,
                                                       inputFrameworkStringNameToTest: String,
                                                       targetValue: String,
                                                       trueIndex: Int,
                                                       falseIndex: Int,

            Method conditionalFieldValueIntIndexArrayRule has 7 arguments (exceeds 4 allowed). Consider refactoring.
            Open

            fun conditionalFieldValueIntIndexArrayRule(outputAttribute: String,
                                                       inputFrameworkStringNameToTest: String,
                                                       targetValue: String,
                                                       trueIndex: Int,
                                                       falseIndex: Int,

              Method listAttributeValueLookupToIndex has 5 arguments (exceeds 4 allowed). Consider refactoring.
              Open

              fun listAttributeValueLookupToIndex(outputAttributeValue: String, inputAttributeValue: String, idx: Int,argumentIndex: Int,defaultValueIfNotFound: OpNamespace.ArgDescriptor? = null): TensorflowListAttributeValueLookupToIndex {

                Method mapStringToInt has 5 arguments (exceeds 4 allowed). Consider refactoring.
                Open

                fun mapStringToInt(outputAttributeValue: String, inputAttributeValue: String, mapOfValuesToInts: Map<String,Int>,argumentIndex: Int,lookupIndex:Int): TensorflowMapStringToInt {

                  Avoid too many return statements within this method.
                  Open

                              return  TensorProto {
                                  val totalLength = ArrayUtil.prod(*arrayToConvert.shape())
                                  val stringList = ArrayList<String>()
                                  for(i in 0 until totalLength) {
                                      val currString = arrayToConvert.getString(i.toLong())

                    Avoid too many return statements within this method.
                    Open

                                return  TensorProto {
                                    DoubleData(arrayToConvert.data().asDouble().toList())
                                    Shape(arrayToConvert.shape().toList())
                                    dtype = DataType.DT_DOUBLE
                                }

                      Avoid too many return statements within this method.
                      Open

                                  return  TensorProto {
                                      dtype = convertNd4jDataTypeToTensorflow(arrayToConvert.dataType())
                                      RawData(arrayToConvert.data().asBytes())
                                      Shape(arrayToConvert.shape().toList())
                      
                      

                        Avoid too many return statements within this method.
                        Open

                                    return  TensorProto {
                                        Int64Data(arrayToConvert.data().asLong().toList())
                                        Shape(arrayToConvert.shape().toList())
                                        dtype = DataType.DT_INT64
                                    }

                          Avoid too many return statements within this method.
                          Open

                                      return  TensorProto {
                                          Int32Data(arrayToConvert.data().asInt().toList())
                                          Shape(arrayToConvert.shape().toList())
                                          dtype = DataType.DT_UINT32
                                      }

                            Avoid too many return statements within this method.
                            Open

                                        return TensorProto {
                                            FloatData(arrayToConvert.data().asFloat().toList())
                                            Shape(arrayToConvert.shape().toList())
                                            dtype = DataType.DT_FLOAT
                                        }

                              Avoid too many return statements within this method.
                              Open

                                          return  TensorProto {
                                              Int32Data(arrayToConvert.data().asInt().toList())
                                              Shape(arrayToConvert.shape().toList())
                                              dtype = DataType.DT_INT32
                                          }

                                Similar blocks of code found in 2 locations. Consider refactoring.
                                Open

                                fun conditionalFieldValueIntIndexArrayRule(outputAttribute: String,
                                                                           inputFrameworkStringNameToTest: String,
                                                                           targetValue: String,
                                                                           trueIndex: Int,
                                                                           falseIndex: Int,
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 144..175

                                Duplicated Code

                                Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

                                Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

                                When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

                                Tuning

                                This issue has a mass of 381.

                                We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

                                The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

                                If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

                                See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

                                Refactorings

                                Further Reading

                                Similar blocks of code found in 2 locations. Consider refactoring.
                                Open

                                fun conditionalFieldValueIntIndexNDArrayRule(outputAttribute: String,
                                                                             inputFrameworkStringNameToTest: String,
                                                                             targetValue: String,
                                                                             trueIndex: Int,
                                                                             falseIndex: Int,
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 178..209

                                Duplicated Code

                                Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

                                Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

                                When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

                                Tuning

                                This issue has a mass of 381.

                                We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

                                The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

                                If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

                                See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

                                Refactorings

                                Further Reading

                                Similar blocks of code found in 2 locations. Consider refactoring.
                                Open

                                fun mapStringToInt(outputAttributeValue: String, inputAttributeValue: String, mapOfValuesToInts: Map<String,Int>,argumentIndex: Int,lookupIndex:Int): TensorflowMapStringToInt {
                                    return TensorflowMapStringToInt(mappingNamesToPerform = mapOf(outputAttributeValue to inputAttributeValue),transformerArgs =
                                    mapOf(outputAttributeValue to mapOfValuesToInts.map {
                                            entry -> ArgDescriptor {
                                        name = entry.key
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 219..231

                                Duplicated Code

                                Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

                                Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

                                When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

                                Tuning

                                This issue has a mass of 311.

                                We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

                                The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

                                If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

                                See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

                                Refactorings

                                Further Reading

                                Similar blocks of code found in 2 locations. Consider refactoring.
                                Open

                                fun ndarrayExtractScalarValue(outputAttribute: String,
                                                              inputFrameworkAttributeName: String,
                                                              argumentIndex: Int,
                                                              scalarIndex: Int): TensorflowNDArrayExtractScalarValue {
                                    return TensorflowNDArrayExtractScalarValue(
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 75..88

                                Duplicated Code

                                Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

                                Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

                                When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

                                Tuning

                                This issue has a mass of 211.

                                We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

                                The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

                                If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

                                See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

                                Refactorings

                                Further Reading

                                Similar blocks of code found in 2 locations. Consider refactoring.
                                Open

                                fun ndarrayStringToIndex(outputAttributeValue: String,inputAttributeValue: String, listOfValues: List<String>,argumentIndex: Int): TensorflowNdArrayToStringIndex {
                                    return TensorflowNdArrayToStringIndex(mappingNamesToPerform = mapOf(outputAttributeValue to inputAttributeValue),transformerArgs = mapOf(outputAttributeValue to listOfValues.map {
                                            valueName -> ArgDescriptor {
                                        name = valueName
                                        stringValue = valueName
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 207..216

                                Duplicated Code

                                Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

                                Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

                                When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

                                Tuning

                                This issue has a mass of 205.

                                We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

                                The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

                                If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

                                See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

                                Refactorings

                                Further Reading

                                Similar blocks of code found in 8 locations. Consider refactoring.
                                Open

                                        org.nd4j.linalg.api.buffer.DataType.UINT8 -> {
                                            return  TensorProto {
                                                Int32Data(arrayToConvert.data().asInt().toList())
                                                Shape(arrayToConvert.shape().toList())
                                                dtype = DataType.DT_UINT8
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 36..42
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 45..51
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 61..67
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 69..75
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 76..82
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 83..89
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 90..96

                                Duplicated Code

                                Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

                                Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

                                When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

                                Tuning

                                This issue has a mass of 136.

                                We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

                                The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

                                If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

                                See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

                                Refactorings

                                Further Reading

                                Similar blocks of code found in 8 locations. Consider refactoring.
                                Open

                                        org.nd4j.linalg.api.buffer.DataType.FLOAT16 -> {
                                            return  TensorProto {
                                                FloatData(arrayToConvert.data().asFloat().toList())
                                                Shape(arrayToConvert.shape().toList())
                                                dtype = DataType.DT_HALF
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 45..51
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 53..59
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 61..67
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 69..75
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 76..82
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 83..89
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 90..96

                                Duplicated Code

                                Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

                                Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

                                When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

                                Tuning

                                This issue has a mass of 136.

                                We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

                                The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

                                If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

                                See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

                                Refactorings

                                Further Reading

                                Similar blocks of code found in 8 locations. Consider refactoring.
                                Open

                                        org.nd4j.linalg.api.buffer.DataType.UINT64-> {
                                            return  TensorProto {
                                                Int64Data(arrayToConvert.data().asLong().toList())
                                                Shape(arrayToConvert.shape().toList())
                                                dtype = DataType.DT_UINT64
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 36..42
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 53..59
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 61..67
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 69..75
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 76..82
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 83..89
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 90..96

                                Duplicated Code

                                Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

                                Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

                                When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

                                Tuning

                                This issue has a mass of 136.

                                We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

                                The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

                                If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

                                See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

                                Refactorings

                                Further Reading

                                Similar blocks of code found in 8 locations. Consider refactoring.
                                Open

                                        org.nd4j.linalg.api.buffer.DataType.FLOAT -> {
                                            return TensorProto {
                                                FloatData(arrayToConvert.data().asFloat().toList())
                                                Shape(arrayToConvert.shape().toList())
                                                dtype = DataType.DT_FLOAT
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 36..42
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 45..51
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 53..59
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 61..67
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 76..82
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 83..89
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 90..96

                                Duplicated Code

                                Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

                                Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

                                When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

                                Tuning

                                This issue has a mass of 136.

                                We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

                                The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

                                If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

                                See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

                                Refactorings

                                Further Reading

                                Similar blocks of code found in 8 locations. Consider refactoring.
                                Open

                                        org.nd4j.linalg.api.buffer.DataType.DOUBLE -> {
                                            return  TensorProto {
                                                DoubleData(arrayToConvert.data().asDouble().toList())
                                                Shape(arrayToConvert.shape().toList())
                                                dtype = DataType.DT_DOUBLE
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 36..42
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 45..51
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 53..59
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 61..67
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 69..75
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 76..82
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 83..89

                                Duplicated Code

                                Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

                                Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

                                When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

                                Tuning

                                This issue has a mass of 136.

                                We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

                                The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

                                If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

                                See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

                                Refactorings

                                Further Reading

                                Similar blocks of code found in 8 locations. Consider refactoring.
                                Open

                                        org.nd4j.linalg.api.buffer.DataType.INT64 -> {
                                            return  TensorProto {
                                                Int64Data(arrayToConvert.data().asLong().toList())
                                                Shape(arrayToConvert.shape().toList())
                                                dtype = DataType.DT_INT64
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 36..42
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 45..51
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 53..59
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 61..67
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 69..75
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 76..82
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 90..96

                                Duplicated Code

                                Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

                                Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

                                When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

                                Tuning

                                This issue has a mass of 136.

                                We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

                                The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

                                If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

                                See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

                                Refactorings

                                Further Reading

                                Similar blocks of code found in 8 locations. Consider refactoring.
                                Open

                                        org.nd4j.linalg.api.buffer.DataType.INT32 -> {
                                            return  TensorProto {
                                                Int32Data(arrayToConvert.data().asInt().toList())
                                                Shape(arrayToConvert.shape().toList())
                                                dtype = DataType.DT_INT32
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 36..42
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 45..51
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 53..59
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 61..67
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 69..75
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 83..89
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 90..96

                                Duplicated Code

                                Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

                                Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

                                When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

                                Tuning

                                This issue has a mass of 136.

                                We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

                                The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

                                If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

                                See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

                                Refactorings

                                Further Reading

                                Similar blocks of code found in 8 locations. Consider refactoring.
                                Open

                                        org.nd4j.linalg.api.buffer.DataType.UINT32 -> {
                                            return  TensorProto {
                                                Int32Data(arrayToConvert.data().asInt().toList())
                                                Shape(arrayToConvert.shape().toList())
                                                dtype = DataType.DT_UINT32
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 36..42
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 45..51
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 53..59
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 69..75
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 76..82
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 83..89
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 90..96

                                Duplicated Code

                                Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

                                Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

                                When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

                                Tuning

                                This issue has a mass of 136.

                                We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

                                The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

                                If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

                                See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

                                Refactorings

                                Further Reading

                                Similar blocks of code found in 3 locations. Consider refactoring.
                                Open

                                fun argDescriptorConstant(argDescriptorConstants: List<OpNamespace.ArgDescriptor>): TensorflowArgDescriptorConstant {
                                    return TensorflowArgDescriptorConstant(mappingNamesToPerform = emptyMap(),transformerArgs = mapOf("value" to argDescriptorConstants))
                                }
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 292..294
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 381..383

                                Duplicated Code

                                Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

                                Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

                                When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

                                Tuning

                                This issue has a mass of 98.

                                We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

                                The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

                                If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

                                See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

                                Refactorings

                                Further Reading

                                Similar blocks of code found in 3 locations. Consider refactoring.
                                Open

                                fun ndarrayAttributeToScalarAttribute(argDescriptorConstants: List<OpNamespace.ArgDescriptor>): TensorflowAttributeNDArrayToScalarAttribute {
                                    return TensorflowAttributeNDArrayToScalarAttribute(mappingNamesToPerform = emptyMap(),transformerArgs = mapOf("value" to argDescriptorConstants))
                                }
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 292..294
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 376..378

                                Duplicated Code

                                Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

                                Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

                                When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

                                Tuning

                                This issue has a mass of 98.

                                We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

                                The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

                                If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

                                See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

                                Refactorings

                                Further Reading

                                Similar blocks of code found in 9 locations. Consider refactoring.
                                Open

                                fun listNumberToListNumber(outputAttributeValue: String, inputAttributeValue: String): TensorflowListNumberToListNumber {
                                    return TensorflowListNumberToListNumber(mappingNamesToPerform = mapOf(outputAttributeValue to inputAttributeValue),transformerArgs = emptyMap())
                                }
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 256..258
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 261..263
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 266..268
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 273..275
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 278..280
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 283..285
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 287..289
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 332..334

                                Duplicated Code

                                Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

                                Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

                                When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

                                Tuning

                                This issue has a mass of 89.

                                We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

                                The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

                                If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

                                See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

                                Refactorings

                                Further Reading

                                Similar blocks of code found in 9 locations. Consider refactoring.
                                Open

                                fun convertNumberListToInputNDArray(outputAttributeValue: String, inputAttributeValue: String): TensorflowAttributeNumberListNDArray {
                                    return TensorflowAttributeNumberListNDArray(mappingNamesToPerform = mapOf(outputAttributeValue to inputAttributeValue),transformerArgs = emptyMap())
                                }
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 256..258
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 261..263
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 266..268
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 273..275
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 278..280
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 283..285
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 287..289
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 323..325

                                Duplicated Code

                                Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

                                Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

                                When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

                                Tuning

                                This issue has a mass of 89.

                                We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

                                The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

                                If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

                                See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

                                Refactorings

                                Further Reading

                                Similar blocks of code found in 11 locations. Consider refactoring.
                                Open

                                fun convertNDArrayInputToNumericalAttr(mutableMap: MutableMap<String,String>): TensorflowNDArrayInputToNumericalAttribute {
                                    return TensorflowNDArrayInputToNumericalAttribute(mappingNamesToPerform = mutableMap,transformerArgs = emptyMap())
                                }
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 117..119
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 121..123
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 125..130
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 133..135
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 284..286
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 288..290
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 327..329
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 357..359
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 366..368
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 371..373

                                Duplicated Code

                                Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

                                Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

                                When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

                                Tuning

                                This issue has a mass of 84.

                                We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

                                The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

                                If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

                                See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

                                Refactorings

                                Further Reading

                                Similar blocks of code found in 11 locations. Consider refactoring.
                                Open

                                fun convertStringToInputNDArray(mappings: Map<String,String>): TensorflowStringAttributeToNDArray {
                                    return TensorflowStringAttributeToNDArray(mappingNamesToPerform = mappings,transformerArgs = emptyMap())
                                }
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 117..119
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 121..123
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 125..130
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 133..135
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 284..286
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 288..290
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 357..359
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 362..364
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 366..368
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 371..373

                                Duplicated Code

                                Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

                                Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

                                When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

                                Tuning

                                This issue has a mass of 84.

                                We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

                                The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

                                If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

                                See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

                                Refactorings

                                Further Reading

                                Similar blocks of code found in 11 locations. Consider refactoring.
                                Open

                                fun invertBooleanNumber(mappings: Map<String,String>): TensorflowInvertBooleanNumber {
                                    return TensorflowInvertBooleanNumber(mappingNamesToPerform = mappings,transformerArgs = emptyMap())
                                }
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 117..119
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 121..123
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 125..130
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 133..135
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 284..286
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 327..329
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 357..359
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 362..364
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 366..368
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 371..373

                                Duplicated Code

                                Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

                                Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

                                When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

                                Tuning

                                This issue has a mass of 84.

                                We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

                                The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

                                If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

                                See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

                                Refactorings

                                Further Reading

                                Similar blocks of code found in 11 locations. Consider refactoring.
                                Open

                                fun ndArrayAttributeToNDarrayInput(mutableMap: MutableMap<String,String>): TensorflowNDArrayAttributeToNDArrayInput {
                                    return TensorflowNDArrayAttributeToNDArrayInput(mappingNamesToPerform = mutableMap,transformerArgs = emptyMap())
                                }
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 117..119
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 121..123
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 125..130
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 133..135
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 284..286
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 288..290
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 327..329
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 357..359
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 362..364
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 366..368

                                Duplicated Code

                                Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

                                Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

                                When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

                                Tuning

                                This issue has a mass of 84.

                                We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

                                The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

                                If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

                                See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

                                Refactorings

                                Further Reading

                                Identical blocks of code found in 2 locations. Consider refactoring.
                                Open

                                           transformerArgs = mapOf(outputAttributeValue to listOf(ArgDescriptor {
                                               argType = OpNamespace.ArgDescriptor.ArgType.INT64
                                               int64Value = idx.toLong()
                                               name = "index"
                                               argIndex = argumentIndex
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 340..345

                                Duplicated Code

                                Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

                                Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

                                When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

                                Tuning

                                This issue has a mass of 84.

                                We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

                                The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

                                If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

                                See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

                                Refactorings

                                Further Reading

                                Similar blocks of code found in 11 locations. Consider refactoring.
                                Open

                                fun dataTypeToInt(mutableMap: MutableMap<String,String>): TensorflowDataTypeToInt {
                                    return TensorflowDataTypeToInt(mappingNamesToPerform = mutableMap,transformerArgs = emptyMap())
                                }
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 117..119
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 121..123
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 125..130
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 133..135
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 284..286
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 288..290
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 327..329
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 362..364
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 366..368
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 371..373

                                Duplicated Code

                                Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

                                Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

                                When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

                                Tuning

                                This issue has a mass of 84.

                                We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

                                The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

                                If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

                                See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

                                Refactorings

                                Further Reading

                                Identical blocks of code found in 2 locations. Consider refactoring.
                                Open

                                        transformerArgs = mapOf(outputAttributeValue to listOf(ArgDescriptor {
                                            argType = OpNamespace.ArgDescriptor.ArgType.INT64
                                            int64Value = idx.toLong()
                                            name = "index"
                                            argIndex = argumentIndex
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 348..353

                                Duplicated Code

                                Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

                                Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

                                When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

                                Tuning

                                This issue has a mass of 84.

                                We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

                                The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

                                If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

                                See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

                                Refactorings

                                Further Reading

                                Similar blocks of code found in 11 locations. Consider refactoring.
                                Open

                                fun valueMapping(mappings: Map<String,String>): TensorflowValueMappingRule {
                                    return TensorflowValueMappingRule(mappingNamesToPerform = mappings,transformerArgs = emptyMap())
                                }
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 117..119
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 121..123
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 125..130
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 133..135
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 288..290
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 327..329
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 357..359
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 362..364
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 366..368
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 371..373

                                Duplicated Code

                                Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

                                Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

                                When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

                                Tuning

                                This issue has a mass of 84.

                                We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

                                The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

                                If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

                                See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

                                Refactorings

                                Further Reading

                                Similar blocks of code found in 11 locations. Consider refactoring.
                                Open

                                fun listNumberToNDarray(mutableMap: MutableMap<String,String>): TensorflowListNumberToNDArray {
                                    return TensorflowListNumberToNDArray(mappingNamesToPerform = mutableMap,transformerArgs = emptyMap())
                                }
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 117..119
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 121..123
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 125..130
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 133..135
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 284..286
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 288..290
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 327..329
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 357..359
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 362..364
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowRuleDeclarations.kt on lines 371..373

                                Duplicated Code

                                Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

                                Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

                                When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

                                Tuning

                                This issue has a mass of 84.

                                We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

                                The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

                                If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

                                See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

                                Refactorings

                                Further Reading

                                Similar blocks of code found in 6 locations. Consider refactoring.
                                Open

                                fun ndarrayToIntList(ndarrayNameToAttributeName: MutableMap<String,String>): TensorflowNDArrayToIntAttributeValue {
                                    return TensorflowNDArrayToIntAttributeValue(mappingNamesToPerform = ndarrayNameToAttributeName)
                                }
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 33..36
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 39..42
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 182..184
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowProtobufExtensions.kt on lines 201..205
                                nd4j/samediff-import/samediff-import-tensorflow/src/main/kotlin/org/nd4j/samediff/frameworkimport/tensorflow/TensorflowProtobufExtensions.kt on lines 245..249

                                Duplicated Code

                                Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

                                Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

                                When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

                                Tuning

                                This issue has a mass of 69.

                                We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

                                The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

                                If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

                                See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

                                Refactorings

                                Further Reading

                                Similar blocks of code found in 2 locations. Consider refactoring.
                                Open

                                fun listAttributeValueLookupToIndex(outputAttributeValue: String, inputAttributeValue: String, idx: Int,argumentIndex: Int,defaultValueIfNotFound: OpNamespace.ArgDescriptor? = null): TensorflowListAttributeValueLookupToIndex {
                                nd4j/samediff-import/samediff-import-onnx/src/main/kotlin/org/nd4j/samediff/frameworkimport/onnx/OnnxRuleDeclarations.kt on lines 234..234

                                Duplicated Code

                                Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

                                Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

                                When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

                                Tuning

                                This issue has a mass of 68.

                                We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

                                The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

                                If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

                                See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

                                Refactorings

                                Further Reading

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