Defines the right convolutional layer according to the
  version of Keras that is installed.
  :param filters: (required integer) the dimensionality of the output
                  space (i.e. the number output of filters in the
                  convolution)
  :param kernel_shape: (required tuple or list of 2 integers) specifies
                       the kernel shape of the convolution
  :param strides: (required tuple or list of 2 integers) specifies
                       the strides of the convolution along the width and
                       height.
  :param padding: (required string) can be either 'valid' (no padding around
                  input or feature map) or 'same' (pad to ensure that the
                  output feature map size is identical to the layer input)
  :param input_shape: (optional) give input shape if this is the first
                      layer of the model
  :return: the Keras layer

def conv_2d(filters, kernel_shape, strides, padding, input_shape=None):
  """
  Defines the right convolutional layer according to the
  version of Keras that is installed.
  :param filters: (required integer) the dimensionality of the output
                  space (i.e. the number output of filters in the
                  convolution)
  :param kernel_shape: (required tuple or list of 2 integers) specifies
                       the kernel shape of the convolution
  :param strides: (required tuple or list of 2 integers) specifies
                       the strides of the convolution along the width and
                       height.
  :param padding: (required string) can be either 'valid' (no padding around
                  input or feature map) or 'same' (pad to ensure that the
                  output feature map size is identical to the layer input)
  :param input_shape: (optional) give input shape if this is the first
                      layer of the model
  :return: the Keras layer
  """
  if input_shape is not None:
    return Conv2D(filters=filters, kernel_size=kernel_shape,
                  strides=strides, padding=padding,
                  input_shape=input_shape)
  else:
    return Conv2D(filters=filters, kernel_size=kernel_shape,
                  strides=strides, padding=padding)