text: #include "caffe2/operators/log_op.h"
  
  #include <string>
  #include <vector>
  
  namespace caffe2 {
  
  REGISTER_CPU_OPERATOR(
      Log,
      UnaryElementwiseOp<TensorTypes<float>, CPUContext, LogFunctor<CPUContext>>);
  
  OPERATOR_SCHEMA(Log)
      .NumInputs(1)
      .NumOutputs(1)
      .AllowInplace({{0, 0}})
      .IdenticalTypeAndShape()
      .SetDoc(R"DOC(
  Calculates the natural log of the given input tensor ($ln(x)$), element-wise. This
  operation can be done in an in-place fashion too, by providing the same input
  and output blobs.
  
  Github Link:
  - https://github.com/pytorch/pytorch/blob/master/caffe2/operators/log_op.cc
  
  <details>
  
  <summary> <b>Example</b> </summary>
  
  **Code**
  
  ```
  
  workspace.ResetWorkspace()
  
  op = core.CreateOperator(
      "Log",
      ["X"],
      ["X"],
  )
  
  workspace.FeedBlob("X", (np.random.rand(3,3)).astype(np.float32))
  print("X before running op:", workspace.FetchBlob("X"))
  workspace.RunOperatorOnce(op)
  print("X after running op:", workspace.FetchBlob("X"))
  
  ```
  
  **Result**
  
  ```
  
  X before running op:
  [[0.07341351 0.15404125 0.386613  ]
   [0.34090295 0.99727786 0.24141751]
   [0.32016268 0.8724168  0.93515724]]
  X after running op:
  [[-2.6116474  -1.8705349  -0.9503311 ]
   [-1.0761575  -0.00272586 -1.4212275 ]
   [-1.138926   -0.13648799 -0.06704059]]
  
  ```
  
  </details>
  
  )DOC")
      .Input(0, "X", "*(type: Tensor`<float>`)* Input tensor.")
      .Output(
          0,
          "Y",
          "*(type: Tensor`<float>`)* Output tensor computed as the natural log of the input tensor computed, element-wise.")
      .InheritOnnxSchema("Log");
  
  namespace {
  
  class GetLogGradient : public GradientMakerBase {
    using GradientMakerBase::GradientMakerBase;
    std::vector<OperatorDef> GetGradientDefs() override {
      return SingleGradientDef(
          "Div",
          "",
          std::vector<std::string>{GO(0), I(0)},
          std::vector<std::string>{GI(0)});
    }
  };
  
  } // namespace
  
  REGISTER_GRADIENT(Log, GetLogGradient);
  
  } // namespace caffe2
  