original_docstring: Creates a fully connected layer applied to `inputs`.
  
    Args:
      inputs: a tensor that the fully connected layer will be applied to. It
        will be reshaped if it is not 2D.
      num_units_out: the number of output units in the layer.
      scope: Optional scope for variable_scope.
      reuse: whether or not the layer and its variables should be reused.
  
    Returns:
       a tensor that is the result of applying a linear matrix to `inputs`.
    
docstring: Creates a fully connected layer applied to `inputs`.
docstring_tokens: Creates a fully connected layer applied to ` inputs ` .
code: def fc(inputs,
         num_units_out,
         scope=None,
         reuse=None):
    """Creates a fully connected layer applied to `inputs`.
  
    Args:
      inputs: a tensor that the fully connected layer will be applied to. It
        will be reshaped if it is not 2D.
      num_units_out: the number of output units in the layer.
      scope: Optional scope for variable_scope.
      reuse: whether or not the layer and its variables should be reused.
  
    Returns:
       a tensor that is the result of applying a linear matrix to `inputs`.
    """
    if len(inputs.shape) > 2:
      inputs = tf.reshape(inputs, [int(inputs.shape[0]), -1])
  
    with tf.variable_scope(scope, 'FC', [inputs], reuse=reuse):
      num_units_in = inputs.shape[1]
      weights_shape = [num_units_in, num_units_out]
      unif_init_range = 1.0 / (num_units_out)**(0.5)
      weights_initializer = tf.random_uniform_initializer(
          -unif_init_range, unif_init_range)
      weights = variable(
          name='weights',
          shape=weights_shape,
          dtype=tf.float32,
          initializer=weights_initializer,
          trainable=True)
      bias_initializer = tf.constant_initializer(0.0)
      biases = variable(
          name='biases',
          shape=[num_units_out,],
          dtype=tf.float32,
          initializer=bias_initializer,
          trainable=True)
      outputs = tf.nn.xw_plus_b(inputs, weights, biases)
      return outputs
code_tokens: def fc ( inputs , num_units_out , scope = None , reuse = None ) : if len ( inputs . shape ) > 2 : inputs = tf . reshape ( inputs , [ int ( inputs . shape [ 0 ] ) , - 1 ] ) with tf . variable_scope ( scope , 'FC' , [ inputs ] , reuse = reuse ) : num_units_in = inputs . shape [ 1 ] weights_shape = [ num_units_in , num_units_out ] unif_init_range = 1.0 / ( num_units_out ) ** ( 0.5 ) weights_initializer = tf . random_uniform_initializer ( - unif_init_range , unif_init_range ) weights = variable ( name = 'weights' , shape = weights_shape , dtype = tf . float32 , initializer = weights_initializer , trainable = True ) bias_initializer = tf . constant_initializer ( 0.0 ) biases = variable ( name = 'biases' , shape = [ num_units_out , ] , dtype = tf . float32 , initializer = bias_initializer , trainable = True ) outputs = tf . nn . xw_plus_b ( inputs , weights , biases ) return outputs
short_docstring: Creates a fully connected layer applied to `inputs`.
short_docstring_tokens: Creates a fully connected layer applied to ` inputs ` .
comment: """Creates a fully connected layer applied to `inputs`.
  
    Args:
      inputs: a tensor that the fully connected layer will be applied to. It
        will be reshaped if it is not 2D.
      num_units_out: the number of output units in the layer.
      scope: Optional scope for variable_scope.
      reuse: whether or not the layer and its variables should be reused.
  
    Returns:
       a tensor that is the result of applying a linear matrix to `inputs`.
    """