text: #include "caffe2/operators/floor_op.h"
  
  #include "caffe2/utils/math.h"
  
  namespace caffe2 {
  
  REGISTER_CPU_OPERATOR(Floor, FloorOp<float, CPUContext>);
  
  OPERATOR_SCHEMA(Floor)
      .NumInputs(1)
      .NumOutputs(1)
      .AllowInplace({{0, 0}})
      .SetDoc(R"DOC(
  Element-wise application of the floor function ($y=floor(x)$) to the input
  tensor `X`. Output tensor shape is the same as the input tensor. This
  operator can be used in an in-place fashion by using the same input blob as the
  output blob.
  
  Github Link:
  - https://github.com/pytorch/pytorch/blob/master/caffe2/operators/floor_op.cc
  
  <details>
  
  <summary> <b>Example</b> </summary>
  
  **Code**
  
  ```
  
  workspace.ResetWorkspace()
  
  op = core.CreateOperator(
      "Floor",
      ["X"],
      ["X"],
  )
  
  workspace.FeedBlob("X", (np.random.uniform(-10, 10, (5,5))).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:
  [[ 3.813361   -1.319647    5.2089314  -4.931328    0.6218652 ]
   [ 7.2757645   5.5552588   5.785643   -2.4790506  -0.41400087]
   [ 1.1541046  -6.933266    3.3754056   1.6569928  -1.7670316 ]
   [-3.4932013   4.891472    1.5530115  -3.2443287  -4.605099  ]
   [-4.574543   -7.360948    5.91305    -8.196495   -5.357458  ]]
  X after running op:
  [[ 3. -2.  5. -5.  0.]
   [ 7.  5.  5. -3. -1.]
   [ 1. -7.  3.  1. -2.]
   [-4.  4.  1. -4. -5.]
   [-5. -8.  5. -9. -6.]]
  
  ```
  
  </details>
  
  )DOC")
      .Input(0, "X", "*(type: Tensor`<float>`)* Input tensor.")
      .Output(0, "Y", "*(type: Tensor`<float>`)* Output tensor.");
  
  // TODO: Write gradient for this when needed
  GRADIENT_NOT_IMPLEMENTED_YET(Floor);
  
  } // namespace caffe2
  