dtype.bits = TF_DataTypeSize(data_type) * 8;
  switch (data_type) {
    case TF_DataType::TF_BOOL:
      dtype.code = DLDataTypeCode::kDLBool;
      break;
    case TF_DataType::TF_HALF:
    case TF_DataType::TF_FLOAT:
    case TF_DataType::TF_DOUBLE:
      dtype.code = DLDataTypeCode::kDLFloat;
      break;
    case TF_DataType::TF_INT8:
    case TF_DataType::TF_INT16:
    case TF_DataType::TF_INT32:

    TFE_OpSetAttrType(op, "dtype", TF_FLOAT);
    TFE_OpAddInput(op, var_handle, status);
    ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    TFE_Execute(op, &handle_1, &num_retvals, status);
    ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    TFE_DeleteOp(op);

    ASSERT_EQ(1, num_retvals);
    EXPECT_EQ(TF_FLOAT, TFE_TensorHandleDataType(handle_1));

  TF_SetAttrType(placeholder_desc, "dtype", TF_FLOAT);
  TF_Operation* placeholder_op = TF_FinishOperation(placeholder_desc, status);
  if (TF_GetCode(status) != TF_OK) return;
  TF_Output x{placeholder_op, 0};

  TF_OperationDescription* reduce_desc =
      TF_NewOperation(body.get(), "CollectiveReduce", "CollectiveReduce");
  TF_SetAttrType(reduce_desc, "T", TF_FLOAT);
  TF_SetAttrInt(reduce_desc, "group_size", group_size);

  TF_Status* status = TF_NewStatus();
  TFE_Op* op = TFE_NewOp(ctx, "ReadVariableOp", status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_OpSetAttrType(op, "dtype", TF_FLOAT);
  TFE_OpAddInput(op, var, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  int num_retvals = 1;
  TFE_Execute(op, out_value, &num_retvals, status);

                      &DoubleDeallocator, nullptr);
}

TF_Tensor* FloatTensor(float v) {
  const int num_bytes = sizeof(float);
  float* values = new float[1];
  values[0] = v;
  return TF_NewTensor(TF_FLOAT, nullptr, 0, values, num_bytes,
                      &FloatDeallocator, nullptr);
}

// All the *Helper methods are used as a workaround for the restrictions that

  TF_Tensor* tensor_shape = Int32Tensor({37, 1});
  TF_Operation* shape = Const(tensor_shape, func_graph.get(), s.get(), "shape");
  TF_Operation* random =
      RandomUniform(shape, TF_FLOAT, func_graph.get(), s.get());

  TF_Output outputs[] = {{random, 0}};
  *func = TF_GraphToFunction(func_graph.get(), name,
                             /*append_hash_to_fn_name=*/false, -1,