code: def test_depthwise_conv1d(
      self,
      depth_multiplier,
      kernel_size,
      strides,
      padding,
      data_format,
      dilation_rate,
  ):
      layer = layers.DepthwiseConv1D(
          depth_multiplier=depth_multiplier,
          kernel_size=kernel_size,
          strides=strides,
          padding=padding,
          data_format=data_format,
          dilation_rate=dilation_rate,
      )
  
      inputs = np.random.normal(size=[2, 8, 4])
      layer.build(input_shape=inputs.shape)
  
      kernel_shape = layer.kernel.shape
      kernel_weights = np.random.normal(size=kernel_shape)
      bias_weights = np.random.normal(size=(depth_multiplier * 4,))
      layer.kernel.assign(kernel_weights)
      layer.bias.assign(bias_weights)
  
      outputs = layer(inputs)
      expected = np_depthwise_conv1d(
          inputs,
          kernel_weights,
          bias_weights,
          strides=strides,
          padding=padding,
          data_format=data_format,
          dilation_rate=dilation_rate,
      )
      self.assertAllClose(outputs, expected)
  
file_path: keras/keras/layers/convolutional/depthwise_conv_test.py
imports: import numpy
  import pytest
  from absl.testing import parameterized
  from numpy.lib.stride_tricks import as_strided
  from keras import layers
  from keras import testing
label_desc: Use image node_id 1 for calling the DepthwiseConvCorrectnessTest obj's underlying member method code with example usage: obj.test_depthwise_conv1d(depth_multiplier, kernel_size, strides, padding, data_format, dilation_rate) without return types