parameters: (X, channel, kernel_size=3, stack_num=2, activation='ReLU', 
                pool=True, batch_norm=False, name='left0')
return_statement: return X
docstring: The encoder block of U-net.
      
      UNET_left(X, channel, kernel_size=3, stack_num=2, activation='ReLU', 
                pool=True, batch_norm=False, name='left0')
      
      Input
      ----------
          X: input tensor.
          channel: number of convolution filters.
          kernel_size: size of 2-d convolution kernels.
          stack_num: number of convolutional layers.
          activation: one of the `tensorflow.keras.layers` interface, e.g., 'ReLU'.
          pool: True or 'max' for MaxPooling2D.
                'ave' for AveragePooling2D.
                False for strided conv + batch norm + activation.
          batch_norm: True for batch normalization, False otherwise.
          name: prefix of the created keras layers.
          
      Output
      ----------
          X: output tensor.
docstring_summary: The encoder block of U-net.
      
      UNET_left(X, channel, kernel_size=3, stack_num=2, activation='ReLU', 
                pool=True, batch_norm=False, name='left0')
      
      Input
      ----------
          X: input tensor.
          channel: number of convolution filters.
          kernel_size: size of 2-d convolution kernels.
          stack_num: number of convolutional layers.
          activation: one of the `tensorflow.keras.layers` interface, e.g., 'ReLU'.
          pool: True or 'max' for MaxPooling2D.
                'ave' for AveragePooling2D.
                False for strided conv + batch norm + activation.
          batch_norm: True for batch normalization, False otherwise.
          name: prefix of the created keras layers.
          
      Output
      ----------
          X: output tensor.
docstring_tokens: The encoder block of U - net . UNET_left ( X channel kernel_size = 3 stack_num = 2 activation = ReLU pool = True batch_norm = False name = left0 ) Input ---------- X : input tensor . channel : number of convolution filters . kernel_size : size of 2 - d convolution kernels . stack_num : number of convolutional layers . activation : one of the tensorflow . keras . layers interface e . g . ReLU . pool : True or max for MaxPooling2D . ave for AveragePooling2D . False for strided conv + batch norm + activation . batch_norm : True for batch normalization False otherwise . name : prefix of the created keras layers . Output ---------- X : output tensor .
function: def UNET_left(X, channel, kernel_size=3, stack_num=2, activation='ReLU', 
                pool=True, batch_norm=False, name='left0'):
      '''
      The encoder block of U-net.
      
      UNET_left(X, channel, kernel_size=3, stack_num=2, activation='ReLU', 
                pool=True, batch_norm=False, name='left0')
      
      Input
      ----------
          X: input tensor.
          channel: number of convolution filters.
          kernel_size: size of 2-d convolution kernels.
          stack_num: number of convolutional layers.
          activation: one of the `tensorflow.keras.layers` interface, e.g., 'ReLU'.
          pool: True or 'max' for MaxPooling2D.
                'ave' for AveragePooling2D.
                False for strided conv + batch norm + activation.
          batch_norm: True for batch normalization, False otherwise.
          name: prefix of the created keras layers.
          
      Output
      ----------
          X: output tensor.
          
      '''
      pool_size = 2
      
      X = encode_layer(X, channel, pool_size, pool, activation=activation, 
                       batch_norm=batch_norm, name='{}_encode'.format(name))
  
      X = CONV_stack(X, channel, kernel_size, stack_num=stack_num, activation=activation, 
                     batch_norm=batch_norm, name='{}_conv'.format(name))
      
      return X
function_tokens: def UNET_left ( X , channel , kernel_size = 3 , stack_num = 2 , activation = 'ReLU' , pool = True , batch_norm = False , name = 'left0' ) : pool_size = 2 X = encode_layer ( X , channel , pool_size , pool , activation = activation , batch_norm = batch_norm , name = '{}_encode' . format ( name ) ) X = CONV_stack ( X , channel , kernel_size , stack_num = stack_num , activation = activation , batch_norm = batch_norm , name = '{}_conv' . format ( name ) ) return X