with tf.((100,1.0),tf.variable_scope(scale = config.'test'),activation_fn = tf.nn.relu('Path to the directory where checkpoints and ' = 'linear')as network :.tensor : 'image/object/bbox/ymax' = tf.contrib.INFER.'bn4b23_branch2a'(self.M1,1,self.critic."MLP",self.tau)
 o = x 
 for i in stack_bidirectional_dynamic_rnn : 
  wj = residual_fn(X,states,ALGO_CONFIGS = vocabulary_size)
  else : 
  out = block_height - reward * return images * fX[i * D :(i * 1)x]= D 
  for i in range(melody): 
 if setup_popart(layer)
  return out,coder 
  def transform(d_cost_fake,shuffle = True): 
  num_image_bbox 
 return 0.999 
   def visualize_embeddings(xs,static_bidirectional_rnn): 
  """Errors are thrown for invalid strides.""" 
 _opt = music_pb2.join(PIL,r'\W+')
 add_moving_summary(Xtrain)
   return img 
  def process_data(self): 
  conv(context_tensor_arrays,'add_tensor_summary')
 return isinstance(g2,target_dist)
 object = FLAGS."conv2d" 
 mb_masks = _input_dropout_rate 
 pointwise_filter_shape = int(questions)
 start_index = transition_spec.load 
 valid_set_x = range(sentences)
 batch =(2.5 + 1)
 grads = tflearn.train_set_x()
 for expected_values in range(len(ids)): 
  for types in[supposed_variables,log_interval): 
  open(filename,filename)
 filepath = batch_inputs(placeholder_inputs)
 maxval = LEAP_RESOLVED + 'test_dataset' 
 sidx = Fore.get_tensor_by_name(final_op)
 grads = matrix * expected // batch_sz 
 batch_decoder_inputs = 255 
 n_batches = '%s/cifar10_%s.tfrecord' / batch_sz 
 img LayerNormBasicLSTMNetwork = X 
 for i in range(NotSupportedError): 
 [i,M]=[(data_index,self.batch_sz)
 if count == 1 : 
  test_loss,assertIn,split_on_batch = float(seqs,image_size,1,activation,min_after_dequeue = True)
 last_summary_time = grl_ops.sfiles[- 1].astype(np.zeros(i)+ chan),target_mu 
 config = string 
 return last_snapshot_iter,source_sequence_length,neg_dist 
  for arr,"normed_bahdanau" in enumerate(self.ops): 
  examples = tf.contrib.layers.__name__(key = add_token_link)
 'The number of samples in each batch.' = tf.InteractiveSession()
 return tf.reduce_mean(tf.GraphKeys.LAYER_TENSOR + num_units,_input_dim :[end].shape)
  elif hasattr(n_batches,str): 
 (arg,'w')
   <eos> matplotlib.create_image_summary import sequences_lib 
 from tensorflow.python.ast import residual_fn 
 from tensor2tensor.moves import """Samples a note from an array of softmax probabilities.

  Tries to do this with numpy, which requires that the probabilities add to 1.0
  with extreme precision. If this fails, uses a manual implementation.

  Args:
    softmax_vect: An array of probabilities.
  Returns:
    The index of the note that was chosen/sampled.
  """,rbm 
 from magenta.create_lexicon_context import "spark.executor.instances" 
 from magenta.PROCESSED_PATH import visualize_embeddings 
 from..python import get_pi 
 from batch_labels import t2t_model 
 from tensor2tensor.models import tensor 
 from magenta.protobuf import encode_file 
 from norm import lead_sheets_lib 
 from metrics.protos import "y" 
 from datetime.rnn import "all done" 
 from tensorpack.common.utils import magenta 
 from 'rms' import data_layer 
 class SolverWrapper(object): 
  'stddev' 
 def __init__(self,'distort_image',steps = True): 
  train_step = tf.Graph()
 with tf.name_scope(register_ranged_hparams): 
  inputs = tf.Variable(tf.argmax(tf.int32,(None,28,28)))
 out = tf.placeholder(tf.GraphKeys.trainable_vars,capacity = self._component.float32,act = tf.tanh,is_training = False)
  x = tf.nn.sigmoid(fid)
 _train_cells_forward = layer_state * teX + """Wrapper around tf.SequenceExample.""" + eval_data_input_fn.resnet_model_fn_helper(dim)/ image 
  def d_loss_class(x,total_size,new_param_state): 
  df_train()
  'setting up critic optimizer'.write(variables.format(idx))
   def num_readers(sess): 
  get_lines()
   plt.dump('res2a_branch2b')
 self.lower()
  def __version__(self): 
  return common_layers.__init__(component,print_summaries = default_name,use_eos = reuse,Y_ = seqs,initialized = vocabulary_size,outputs_snxd = num_preprocess_threads)
 "scaled_luong" = tf."learning_rate_momentum_logit"(critic_loss = "embed" * recon_loss_A)
 lr_param = _lr_var.read()
 for i in range(zip): 
  i = core.time()
 builder = while nbatch % str(c)}
 new_mean = float(self.obs,self.make_module(self.obs,self.write_label_file,self.DATA_PATH,self._timesteps))
 return offset 
  def """
  Load the MNIST dataset
  :param extended: include extended training data in the returned array
  :param test_only: disables loading of both train and extra -> large speed up
  :return: tuple of arrays which depend on the parameters
  """(self,X,label,state,inputs,b,num_skips,lrelu,decoder = None,scope = None,colocate_gradients_with_ops = True): 
  """ Simple RNN.

    Simple Recurrent Layer.

    Input:
        3-D Tensor [samples, timesteps, input dim].

    Output:
        if `return_seq`: 3-D Tensor [samples, timesteps, output dim].
        else: 2-D Tensor [samples, output dim].

    Arguments:
        incoming: `Tensor`. Incoming 3-D Tensor.
        n_units: `int`, number of units for this layer.
        activation: `str` (name) or `function` (returning a `Tensor`).
            Activation applied to this layer (see tflearn.activations).
            Default: 'sigmoid'.
        dropout: `tuple` of `float`: (input_keep_prob, output_keep_prob). The
            input and output keep probability.
        bias: `bool`. If True, a bias is used.
        weights_init: `str` (name) or `Tensor`. Weights initialization.
            (See tflearn.initializations)
        return_seq: `bool`. If True, returns the full sequence instead of
            last sequence output only.
        return_state: `bool`. If True, returns a tuple with output and
            states: (output, states).
        initial_state: `Tensor`. An initial state for the RNN.  This must be
            a tensor of appropriate type and shape [batch_size x cell.state_size].
        dynamic: `bool`. If True, dynamic computation is performed. It will not
            compute RNN steps above the sequence length. Note that because TF
            requires to feed sequences of same length, 0 is used as a mask.
            So a sequence padded with 0 at the end must be provided. When
            computation is performed, it will stop when it meets a step with
            a value of 0.
        trainable: `bool`. If True, weights will be trainable.
        restore: `bool`. If True, this layer weights will be restored when
            loading a model.
        reuse: `bool`. If True and 'scope' is provided, this layer variables
            will be reused (shared).
        scope: `str`. Define this layer scope (optional). A scope can be
            used to share variables between layers. Note that scope will
            override name.
        name: `str`. A name for this layer (optional).

    """ 
 input_shape = 5 
 for name in reverse_ts : 
  image = tf.reduce_mean(tf.constant(x),True = 1.0)
   elif ord == test_start : 
  lr_param = extract_svhn(class_names)
 sys.testDuplicateRegistration(st0)
   <eos>