  void Compute(OpKernelContext* ctx) override {
    const Tensor& indices_tensor = ctx->input(0);
    OP_REQUIRES(ctx,
                TensorShapeUtils::IsVector(indices_tensor.shape()) ||
                    TensorShapeUtils::IsScalar(indices_tensor.shape()),
                errors::InvalidArgument(
                    "The indices can only be scalar or vector, got \"",
                    indices_tensor.shape().DebugString(), "\""));

    const Tensor& dims_tensor = ctx->input(1);
    OP_REQUIRES(
        ctx, TensorShapeUtils::IsVector(dims_tensor.shape()),
        errors::InvalidArgument("The indices can only be 1-D, got \"",
                                dims_tensor.shape().DebugString(), "\""));

    auto dims = dims_tensor.vec<Tidx>();
    // Make sure dims does not contain a zero
    for (int i = 0; i < dims.size(); i++) {
      OP_REQUIRES(
          ctx, dims(i) != 0,
          errors::InvalidArgument("Input dims cannot contain a dim of zero, "
                                  "but dims contains zero at index ",
                                  i));
    }

    // Chek to make sure indices is not out of boundary
    Eigen::Tensor<Tidx, 0, Eigen::RowMajor> dims_prod_eigen = dims.prod();
    Tidx dims_prod = dims_prod_eigen();
    const Tidx* indices = indices_tensor.flat<Tidx>().data();
    int64 size = indices_tensor.NumElements();
    bool check = std::all_of(indices, indices + size,
                             [&](Tidx index) { return index < dims_prod; });
    OP_REQUIRES(ctx, check,
                errors::InvalidArgument("index is out of bound as with dims"));

    Eigen::array<bool, 1> reverse({true});

    Tensor strides_tensor;
    OP_REQUIRES_OK(ctx,
                   ctx->allocate_temp(DataTypeToEnum<Tidx>::value,
                                      TensorShape({dims_tensor.NumElements()}),
                                      &strides_tensor));

    auto strides = strides_tensor.vec<Tidx>();
    strides = dims.reverse(reverse)
                  .scan(0, Eigen::internal::ProdReducer<Tidx>(), false)
                  .reverse(reverse);

    Tensor strides_shifted_tensor;
    OP_REQUIRES_OK(ctx,
                   ctx->allocate_temp(DataTypeToEnum<Tidx>::value,
                                      TensorShape({dims_tensor.NumElements()}),
                                      &strides_shifted_tensor));

    auto strides_shifted = strides_shifted_tensor.vec<Tidx>();
    strides_shifted = dims.reverse(reverse)
                          .scan(0, Eigen::internal::ProdReducer<Tidx>(), true)
                          .reverse(reverse);

    Tensor* output_tensor = nullptr;
    if (TensorShapeUtils::IsScalar(indices_tensor.shape())) {
      OP_REQUIRES_OK(
          ctx, ctx->allocate_output(0, TensorShape({dims_tensor.NumElements()}),
                                    &output_tensor));

      auto output = output_tensor->vec<Tidx>();

      output = output.constant(indices_tensor.scalar<Tidx>()());
      output = output.binaryExpr(strides, mod_op<Tidx>()) / strides_shifted;
    } else {
      OP_REQUIRES_OK(
          ctx, ctx->allocate_output(0,
                                    TensorShape({dims_tensor.NumElements(),
                                                 indices_tensor.NumElements()}),
                                    &output_tensor));

      auto output = output_tensor->matrix<Tidx>();

      Eigen::array<Eigen::Index, 2> reshape{
          {static_cast<Eigen::Index>(dims_tensor.NumElements()), 1}};
      Eigen::array<Eigen::Index, 2> bcast(
          {1, static_cast<Eigen::Index>(indices_tensor.NumElements())});
      Eigen::array<Eigen::Index, 2> indices_reshape{
          {1, static_cast<Eigen::Index>(indices_tensor.NumElements())}};
      Eigen::array<Eigen::Index, 2> indices_bcast(
          {static_cast<Eigen::Index>(dims_tensor.NumElements()), 1});

      output = indices_tensor.vec<Tidx>()
                   .reshape(indices_reshape)
                   .broadcast(indices_bcast);
      output = output.binaryExpr(strides.reshape(reshape).broadcast(bcast),
                                 mod_op<Tidx>()) /
               strides_shifted.reshape(reshape).broadcast(bcast);
    }
  }
  void Compute(OpKernelContext* ctx) override {
    const Tensor& indices_tensor = ctx->input(0);
    OP_REQUIRES(ctx,
                TensorShapeUtils::IsVector(indices_tensor.shape()) ||
                    TensorShapeUtils::IsScalar(indices_tensor.shape()),
                errors::InvalidArgument(
                    "The indices can only be scalar or vector, got \"",
                    indices_tensor.shape().DebugString(), "\""));

    const Tensor& dims_tensor = ctx->input(1);
    OP_REQUIRES(
        ctx, TensorShapeUtils::IsVector(dims_tensor.shape()),
        errors::InvalidArgument("The indices can only be 1-D, got \"",
                                dims_tensor.shape().DebugString(), "\""));

    auto dims = dims_tensor.vec<Tidx>();
     
    for (int i = 0; i < dims.size(); i++) {
      OP_REQUIRES(
          ctx, dims(i) != 0,
          errors::InvalidArgument("Input dims cannot contain a dim of zero, "
                                  "but dims contains zero at index ",
                                  i));
    }

     
    Eigen::Tensor<Tidx, 0, Eigen::RowMajor> dims_prod_eigen = dims.prod();
    Tidx dims_prod = dims_prod_eigen();
    const Tidx* indices = indices_tensor.flat<Tidx>().data();
    int64 size = indices_tensor.NumElements();
    bool check = std::all_of(indices, indices + size,
                             [&](Tidx index) { return index < dims_prod; });
    OP_REQUIRES(ctx, check,
                errors::InvalidArgument("index is out of bound as with dims"));

    Eigen::array<bool, 1> reverse({true});

    Tensor strides_tensor;
    OP_REQUIRES_OK(ctx,
                   ctx->allocate_temp(DataTypeToEnum<Tidx>::value,
                                      TensorShape({dims_tensor.NumElements()}),
                                      &strides_tensor));

    auto strides = strides_tensor.vec<Tidx>();
    strides = dims.reverse(reverse)
                  .scan(0, Eigen::internal::ProdReducer<Tidx>(), false)
                  .reverse(reverse);

    Tensor strides_shifted_tensor;
    OP_REQUIRES_OK(ctx,
                   ctx->allocate_temp(DataTypeToEnum<Tidx>::value,
                                      TensorShape({dims_tensor.NumElements()}),
                                      &strides_shifted_tensor));

    auto strides_shifted = strides_shifted_tensor.vec<Tidx>();
    strides_shifted = dims.reverse(reverse)
                          .scan(0, Eigen::internal::ProdReducer<Tidx>(), true)
                          .reverse(reverse);

    Tensor* output_tensor = nullptr;
    if (TensorShapeUtils::IsScalar(indices_tensor.shape())) {
      OP_REQUIRES_OK(
          ctx, ctx->allocate_output(0, TensorShape({dims_tensor.NumElements()}),
                                    &output_tensor));

      auto output = output_tensor->vec<Tidx>();

      output = output.constant(indices_tensor.scalar<Tidx>()());
      output = output.binaryExpr(strides, mod_op<Tidx>()) / strides_shifted;
    } else {
      OP_REQUIRES_OK(
          ctx, ctx->allocate_output(0,
                                    TensorShape({dims_tensor.NumElements(),
                                                 indices_tensor.NumElements()}),
                                    &output_tensor));

      auto output = output_tensor->matrix<Tidx>();

      Eigen::array<Eigen::Index, 2> reshape{
          {static_cast<Eigen::Index>(dims_tensor.NumElements()), 1}};
      Eigen::array<Eigen::Index, 2> bcast(
          {1, static_cast<Eigen::Index>(indices_tensor.NumElements())});
      Eigen::array<Eigen::Index, 2> indices_reshape{
          {1, static_cast<Eigen::Index>(indices_tensor.NumElements())}};
      Eigen::array<Eigen::Index, 2> indices_bcast(
          {static_cast<Eigen::Index>(dims_tensor.NumElements()), 1});

      output = indices_tensor.vec<Tidx>()
                   .reshape(indices_reshape)
                   .broadcast(indices_bcast);
      output = output.binaryExpr(strides.reshape(reshape).broadcast(bcast),
                                 mod_op<Tidx>()) /
               strides_shifted.reshape(reshape).broadcast(bcast);
    }
  }