return th;
}

TFE_TensorHandle* TestScalarTensorHandle(TFE_Context* ctx, int value) {
  int data[] = {value};
  TF_Status* status = TF_NewStatus();
  TF_Tensor* t = TFE_AllocateHostTensor(ctx, TF_INT32, nullptr, 0, status);
  memcpy(TF_TensorData(t), &data[0], TF_TensorByteSize(t));
  TFE_TensorHandle* th = TFE_NewTensorHandleFromTensor(ctx, t, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  // will be blocked till the copy completes. This is the default placement
  // policy.
  TFE_DEVICE_PLACEMENT_SILENT = 2,
  // Placement policy which silently copies int32 tensors but not other dtypes.
  TFE_DEVICE_PLACEMENT_SILENT_FOR_INT32 = 3,
} TFE_ContextDevicePlacementPolicy;
// LINT.ThenChange(//tensorflow/c/eager/immediate_execution_context.h)

// Sets the default execution mode (sync/async). Note that this can be

  TFE_Op* reshape_op = TFE_NewOp(tfe_context_, "Reshape", status_);
  CHECK_EQ(TF_OK, TF_GetCode(status_)) << TF_Message(status_);
  TFE_OpSetAttrType(reshape_op, "T", TF_FLOAT);
  TFE_OpSetAttrType(reshape_op, "Tshape", TF_INT32);
  CheckOutputShapes(reshape_op,
                    /* input_shapes*/ {unknown_shape(), unknown_shape()},
                    /* input_tensors*/ {nullptr, tensor_1X6},

index e54a067..215b0e0 100644
--- a/sysdeps/ieee754/flt-32/k_rem_pio2f.c
+++ b/sysdeps/ieee754/flt-32/k_rem_pio2f.c
@@ -65,7 +65,8 @@ int __kernel_rem_pio2f(float *x, float *y, int e0, int nx, int prec, const int32
 
     /* compute q[0],q[1],...q[jk] */
 	for (i=0;i<=jk;i++) {
-	    for(j=0,fw=0.0;j<=jx;j++) fw += x[j]*f[jx+i-j]; q[i] = fw;
+	    for(j=0,fw=0.0;j<=jx;j++) fw += x[j]*f[jx+i-j];
+	    q[i] = fw;
 	}
 

namespace gradients {

using namespace std;

// ================== Helper functions =================

// Fills data with values [start,end) with given step size.
void Range(vector<int32_t>* data, int32_t start, int32_t end,
           int32_t step = 1) {
  for (int32_t i = start; i < end; i += step) {
    (*data)[i] = i;
  }
}

// Fills out_dims with the dimensions of the given tensor.
void GetDims(const TF_Tensor* t, int64_t* out_dims) {

  while (n > 0) {
    // Some platforms, notably macs, throw `EINVAL` if `pread` is asked to read
    // more than fits in a 32-bit integer.
    size_t requested_read_length;
    if (n > INT32_MAX)
      requested_read_length = INT32_MAX;
    else
      requested_read_length = n;

    // `pread` returns a `ssize_t` on POSIX, but due to interface being
    // cross-platform, return type of `Read` is `int64_t`.

        tf.TensorSpec([], dtypes.int32) ]) def _remote_multiply(a, b): return
        tf.math.multiply(a, b)

        server.register("multiply", _remote_multiply) ```

    *   Example usage to create client: `python client =
        tf.distribute.experimental.rpc.Client.create("grpc", address) a =
        tf.constant(2, dtype=tf.int32) b = tf.constant(3, dtype=tf.int32)

TFE_Op* RecvOp(TFE_Context* ctx, const std::string& op_name,
               const std::string& send_device, const std::string& recv_device,
               tensorflow::uint64 send_device_incarnation);

// Return a 1-D INT32 tensor containing a single value 1.
TFE_TensorHandle* TestAxisTensorHandle(TFE_Context* ctx);

// Return an op taking minimum of `input` long `axis` dimension.
TFE_Op* MinOp(TFE_Context* ctx, TFE_TensorHandle* input,

}

std::unique_ptr<ParallelTensor> ParallelDevice::DeviceIDs(
    TFE_Context* context, TF_Status* status) const {
  std::vector<int32_t> ids;
  ids.reserve(num_underlying_devices());
  for (int i = 0; i < num_underlying_devices(); ++i) {
    ids.push_back(i);
  }
  return ScalarsFromSequence<int32_t>(ids, context, status);
}

absl::optional<std::vector<std::unique_ptr<ParallelTensor>>>

// The calls to TF_AddInput and TF_AddInputList must match (in number,
// order, and type) the op declaration.  For example, the "Concat" op
// has registration:
//   REGISTER_OP("Concat")
//       .Input("concat_dim: int32")
//       .Input("values: N * T")
//       .Output("output: T")
//       .Attr("N: int >= 2")
//       .Attr("T: type");
// that defines two inputs, "concat_dim" and "values" (in that order).