Error when running RBERT In Tensorflow 1.11.0: "Error in py_call_impl(callable, dots$args, dots$keywords)"

Hi,

I am trying to run RBERT in tensorflow on small dataset. I have installed Tensorflow using the miniconda environment. Below is the code which throws the error:

```
Sys.setenv(RETICULATE_PYTHON = "/Users/applemacbookpro/opt/miniconda3/envs/tensorflowa/bin/python")

#Make virtual environment in anaconda

reticulate::conda_list()[[1]][8] %>%
reticulate::use_condaenv(required = TRUE)

#Load the libraries
library(keras)
library(tidyverse)
library(stringr)
library(tidytext)
library(caret)
library(dplyr)
library(tm)
library(RBERT)
library(tensorflow)
library(reticulate)

#Install RBERT

devtools::install("/Users/applemacbookpro/Downloads/RBERT")

#Initiate BERT
BERT_PRETRAINED_DIR <- RBERT::download_BERT_checkpoint(model = "bert_base_uncased")

#Extract tokenized words from agency trainset
BERT_feats <- extract_features(
examples = agency_trainset$agency,
ckpt_dir = BERT_PRETRAINED_DIR,
layer_indexes = 1:12,
)

```

Error in py_call_impl(callable, dots$args, dots$keywords) :
RuntimeError: Evaluation error: ValueError: Tried to convert 'size' to a tensor and failed. Error: Cannot convert a partially known TensorShape to a Tensor: (128, ?).

```

Traceback:
stop(structure(list(message = "RuntimeError: Evaluation error: ValueError: Tried to convert 'size' to a tensor and failed. Error: Cannot convert a partially known TensorShape to a Tensor: (128, ?).",
call = py_call_impl(callable, dots$args, dots$keywords),
cppstack = structure(list(file = "", line = -1L, stack = c("1 reticulate.so 0x000000010773d3de _ZN4Rcpp9exceptionC2EPKcb + 222",
"2 reticulate.so 0x0000000107746245 _ZN4Rcpp4stopERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEE + 53", ...
13.
python_function at call.py#21
12.
fn at #4
11.
_call_model_fn at tpu_estimator.py#1524
10.
call_without_tpu at tpu_estimator.py#1250
9.
_model_fn at tpu_estimator.py#2470
8.
_call_model_fn at estimator.py#1169
7.
_call_model_fn at tpu_estimator.py#2186
6.
predict at estimator.py#551
5.
predict at tpu_estimator.py#2431
4.
raise_errors at error_handling.py#128
3.
predict at tpu_estimator.py#2437
2.
result_iterator$`next`()
1.
extract_features(examples = agency_trainset$agency, ckpt_dir = BERT_PRETRAINED_DIR,
layer_indexes = 1:12, )
```