@shape_type_conversion
def build(self, input_shape):
    if (len(input_shape) < 4):
        raise ValueError('Inputs to `DepthwiseConv2D` should have rank 4. Received input shape:', str(input_shape))
    if (self.data_format == 'channels_first'):
        channel_axis = 1
    else:
        channel_axis = 3
    if (input_shape[channel_axis] is None):
        raise ValueError('The channel dimension of the inputs to `DepthwiseConv2D` should be defined. Found `None`.')
    input_dim = int(input_shape[channel_axis])
    depthwise_kernel_shape = (self.kernel_size[0], self.kernel_size[1], input_dim, self.depth_multiplier)
    self.depthwise_kernel = self.add_weight(shape=depthwise_kernel_shape, initializer=self.depthwise_initializer, name='depthwise_kernel', regularizer=self.depthwise_regularizer, constraint=self.depthwise_constraint)
    if self.use_bias:
        self.bias = self.add_weight(shape=((input_dim * self.depth_multiplier),), initializer=self.bias_initializer, name='bias', regularizer=self.bias_regularizer, constraint=self.bias_constraint)
    else:
        self.bias = None
    self.input_spec = InputSpec(ndim=4, axes={channel_axis: input_dim})
    self.built = True
@shape_type_conversion
def build(self, input_shape):
    if (len(input_shape) < 4):
        raise ValueError('Inputs to `DepthwiseConv2D` should have rank 4. Received input shape:', str(input_shape))
    if (self.data_format == 'channels_first'):
        channel_axis = 1
    else:
        channel_axis = 3
    if (input_shape[channel_axis] is None):
        raise ValueError('The channel dimension of the inputs to `DepthwiseConv2D` should be defined. Found `None`.')
    input_dim = int(input_shape[channel_axis])
    depthwise_kernel_shape = (self.kernel_size[0], self.kernel_size[1], input_dim, self.depth_multiplier)
    self.depthwise_kernel = self.add_weight(shape=depthwise_kernel_shape, initializer=self.depthwise_initializer, name='depthwise_kernel', regularizer=self.depthwise_regularizer, constraint=self.depthwise_constraint)
    if self.use_bias:
        self.bias = self.add_weight(shape=((input_dim * self.depth_multiplier),), initializer=self.bias_initializer, name='bias', regularizer=self.bias_regularizer, constraint=self.bias_constraint)
    else:
        self.bias = None
    self.input_spec = InputSpec(ndim=4, axes={channel_axis: input_dim})
    self.built = True