gistfile1.txt

from __future__ import print_function

def tf__RNN(x, weights, biases):
  try:
    with ag__.function_scope('RNN'):
      do_return = False
      retval_ = None
      x = ag__.converted_call('unstack', tf, ag__.ConversionOptions(recursive=True, verbose=0, strip_decorators=(ag__.convert, ag__.do_not_convert, ag__.converted_call), force_conversion=False, optional_features=ag__.Feature.ALL, internal_convert_user_code=True), (x, timesteps, 1), {})
      lstm_cell = ag__.converted_call(LSTMCell, None, ag__.ConversionOptions(recursive=True, verbose=0, strip_decorators=(ag__.convert, ag__.do_not_convert, ag__.converted_call), force_conversion=False, optional_features=ag__.Feature.ALL, internal_convert_user_code=True), (num_hidden,), {'forget_bias': 1.0})
      outputs, states = ag__.converted_call(static_rnn, None, ag__.ConversionOptions(recursive=True, verbose=0, strip_decorators=(ag__.convert, ag__.do_not_convert, ag__.converted_call), force_conversion=False, optional_features=ag__.Feature.ALL, internal_convert_user_code=True), (lstm_cell, x), {'dtype': tf.float32})
      do_return = True
      retval_ = ag__.converted_call('matmul', tf, ag__.ConversionOptions(recursive=True, verbose=0, strip_decorators=(ag__.convert, ag__.do_not_convert, ag__.converted_call), force_conversion=False, optional_features=ag__.Feature.ALL, internal_convert_user_code=True), (ag__.get_item(outputs, ag__.u_sub(1), opts=ag__.GetItemOpts(element_dtype=None)), ag__.get_item(weights, 'out', opts=ag__.GetItemOpts(element_dtype=None))), {}) + ag__.get_item(biases, 'out', opts=ag__.GetItemOpts(element_dtype=None))
      return retval_
  except:
    ag__.rewrite_graph_construction_error(ag_source_map__)



tf__RNN.autograph_info__ = {}