class_docstr: This class defines a simple convolution with an optional bias term.
  Please consider ``ConvolutionalLayer`` if batch_norm and activation
  are also used.
docstr: :param padding_constant: a constant applied in padded convolution
  (see also tf.pad)
docstr_tok: [":","param","padding_constant",":","a","constant","applied","in","padded","convolution","(","see","also","tf.pad",")"]
code: def __init__(
      self,
      n_output_chns,
      kernel_size=3,
      stride=1,
      dilation=1,
      padding="SAME",
      with_bias=False,
      w_initializer=None,
      w_regularizer=None,
      b_initializer=None,
      b_regularizer=None,
      padding_constant=0,
      name="conv",
  ):
      """
      :param padding_constant: a constant applied in padded convolution
      (see also tf.pad)
      """
  
      super(ConvLayer, self).__init__(name=name)
  
      self.padding = look_up_operations(
          padding.upper(), SUPPORTED_PADDING
      )
      self.n_output_chns = int(n_output_chns)
      self.kernel_size = kernel_size
      self.stride = stride
      self.dilation = dilation
      self.with_bias = with_bias
      self.padding_constant = padding_constant
  
      self.initializers = {
          "w": w_initializer
          if w_initializer
          else default_w_initializer(),
          "b": b_initializer
          if b_initializer
          else default_b_initializer(),
      }
  
      self.regularizers = {"w": w_regularizer, "b": b_regularizer}
  
imports: from __future__ import absolute_import, print_function
  import math
  import numpy
  import tensorflow
  from niftynet.layer import layer_util
  from niftynet.layer.activation import ActiLayer
  from niftynet.layer.base_layer import TrainableLayer
  from niftynet.layer.bn import BNLayer, InstanceNormLayer
  from niftynet.layer.gn import GNLayer
  from niftynet.utilities.util_common import look_up_operations
label_desc: Use image node_id 1 to create a new ConvLayer object from inherited base classes: TrainableLayer with example: obj = ConvLayer(n_output_chns, kernel_size, stride, dilation, padding, with_bias, w_initializer, w_regularizer, b_initializer, b_regularizer, padding_constant, name)