  void Compute(OpKernelContext* ctx) override {
    const Tensor& input_0 = ctx->input(0);
    OP_REQUIRES(ctx, input_0.dtype() == DataTypeToEnum<Tin>::v(),
                errors::InvalidArgument(
                    "Expected tensor of type ",
                    DataTypeString(DataTypeToEnum<Tin>::v()), " but got type ",
                    DataTypeString(input_0.dtype())));
    const Tensor& input_1 = ctx->input(1);
    OP_REQUIRES(ctx, input_1.dtype() == DataTypeToEnum<Tin>::v(),
                errors::InvalidArgument(
                    "Expected tensor of type ",
                    DataTypeString(DataTypeToEnum<Tin>::v()), " but got type ",
                    DataTypeString(input_1.dtype())));
    const Device& eigen_device = ctx->eigen_device<Device>();
    bool error = false;
    bool* const error_ptr = Functor::has_errors ? &error : nullptr;

    // NOTE: Handle three simple cases before building the BinaryOpState, which
    // is relatively expensive for small operations.
    if (input_0.shape() == input_1.shape()) {
      // tensor op tensor with no broadcasting.
      Tensor* out;
      OP_REQUIRES_OK(ctx, ctx->forward_input_or_allocate_output(
                              {0, 1}, 0, input_0.shape(), &out));
      functor::BinaryFunctor<Device, Functor, 1>()(
          eigen_device, out->template flat<Tout>(),
          input_0.template flat<Tin>(), input_1.template flat<Tin>(),
          error_ptr);
      if (Functor::has_errors && error) {
        SetComputeError(ctx);
      }
      return;
    } else if (input_0.shape().dims() == 0) {
      // scalar op tensor.
      Tensor* out;
      OP_REQUIRES_OK(ctx, ctx->forward_input_or_allocate_output(
                              {1}, 0, input_1.shape(), &out));

      functor::BinaryFunctor<Device, Functor, 1>().Left(
          eigen_device, out->template flat<Tout>(),
          input_0.template scalar<Tin>(), input_1.template flat<Tin>(),
          error_ptr);
      if (Functor::has_errors && error) {
        SetComputeError(ctx);
      }
      return;
    } else if (input_1.shape().dims() == 0) {
      // tensor op scalar.
      Tensor* out;
      OP_REQUIRES_OK(ctx, ctx->forward_input_or_allocate_output(
                              {0}, 0, input_0.shape(), &out));
      functor::BinaryFunctor<Device, Functor, 1>().Right(
          eigen_device, out->template flat<Tout>(),
          input_0.template flat<Tin>(), input_1.template scalar<Tin>(),
          error_ptr);
      if (Functor::has_errors && error) {
        SetComputeError(ctx);
      }
      return;
    }

    // 'state': Shared helper not dependent on T to reduce code size
    BinaryOpState state(ctx);
    if (ctx->status().code() == error::RESOURCE_EXHAUSTED) {
      // Stop when BinaryOpState's constructor failed due to OOM.
      return;
    }
    auto& bcast = state.bcast;
    Tensor* out = state.out;
    if (!bcast.IsValid()) {
      if (ctx->status().ok()) {
        if (state.result) {
          functor::SetOneFunctor<Device, bool>()(eigen_device,
                                                 out->flat<bool>());
        } else {
          functor::SetZeroFunctor<Device, bool>()(eigen_device,
                                                  out->flat<bool>());
        }
      }
      return;
    }

    auto& in0 = state.in0;
    auto& in1 = state.in1;
    if (state.out_num_elements == 0) {
      return;
    }

    const int ndims = state.ndims;
    if (ndims <= 1) {
      auto out_flat = out->flat<Tout>();
      if (state.in1_num_elements == 1) {
        // tensor op scalar
        functor::BinaryFunctor<Device, Functor, 1>().Right(
            eigen_device, out_flat, in0.template flat<Tin>(),
            in1.template scalar<Tin>(), error_ptr);
      } else if (state.in0_num_elements == 1) {
        // scalar op tensor
        functor::BinaryFunctor<Device, Functor, 1>().Left(
            eigen_device, out_flat, in0.template scalar<Tin>(),
            in1.template flat<Tin>(), error_ptr);
      } else {
        functor::BinaryFunctor<Device, Functor, 1>()(
            eigen_device, out_flat, in0.template flat<Tin>(),
            in1.template flat<Tin>(), error_ptr);
      }
    } else if (ndims == 2) {
      functor::BinaryFunctor<Device, Functor, 2>().BCast(
          eigen_device, out->shaped<Tout, 2>(bcast.result_shape()),
          in0.template shaped<Tin, 2>(bcast.x_reshape()),
          BCast::ToIndexArray<2>(bcast.x_bcast()),
          in1.template shaped<Tin, 2>(bcast.y_reshape()),
          BCast::ToIndexArray<2>(bcast.y_bcast()), error_ptr);
    } else if (ndims == 3) {
      functor::BinaryFunctor<Device, Functor, 3>().BCast(
          eigen_device, out->shaped<Tout, 3>(bcast.result_shape()),
          in0.template shaped<Tin, 3>(bcast.x_reshape()),
          BCast::ToIndexArray<3>(bcast.x_bcast()),
          in1.template shaped<Tin, 3>(bcast.y_reshape()),
          BCast::ToIndexArray<3>(bcast.y_bcast()), error_ptr);
    } else if (ndims == 4) {
      functor::BinaryFunctor<Device, Functor, 4>().BCast(
          eigen_device, out->shaped<Tout, 4>(bcast.result_shape()),
          in0.template shaped<Tin, 4>(bcast.x_reshape()),
          BCast::ToIndexArray<4>(bcast.x_bcast()),
          in1.template shaped<Tin, 4>(bcast.y_reshape()),
          BCast::ToIndexArray<4>(bcast.y_bcast()), error_ptr);
    } else if (ndims == 5) {
      functor::BinaryFunctor<Device, Functor, 5>().BCast(
          eigen_device, out->shaped<Tout, 5>(bcast.result_shape()),
          in0.template shaped<Tin, 5>(bcast.x_reshape()),
          BCast::ToIndexArray<5>(bcast.x_bcast()),
          in1.template shaped<Tin, 5>(bcast.y_reshape()),
          BCast::ToIndexArray<5>(bcast.y_bcast()), error_ptr);
    } else {
      SetUnimplementedError(ctx);
    }
    if (Functor::has_errors && error) {
      SetComputeError(ctx);
    }
  }
Unknown