code: def depthwise_conv(
      inputs,
      kernel,
      strides=1,
      padding="valid",
      data_format=None,
      dilation_rate=1,
  ):
      data_format = standardize_data_format(data_format)
      num_spatial_dims = inputs.ndim - 2
      dimension_numbers = _convert_to_lax_conv_dimension_numbers(
          num_spatial_dims,
          data_format,
          transpose=False,
      )
      strides = _convert_to_spatial_operand(
          strides,
          num_spatial_dims,
          data_format,
          include_batch_and_channels=False,
      )
      dilation_rate = _convert_to_spatial_operand(
          dilation_rate,
          num_spatial_dims,
          data_format,
          include_batch_and_channels=False,
      )
      feature_group_count = (
          inputs.shape[-1]
          if data_format == "channels_last"
          else inputs.shape[1]
      )
      kernel = jnp.reshape(
          kernel if is_tensor(kernel) else kernel.numpy(),
          kernel.shape[:-2]
          + (1, feature_group_count * kernel.shape[-1]),
      )
      return np.array(
          jax.lax.conv_general_dilated(
              inputs,
              kernel,
              strides,
              padding,
              rhs_dilation=dilation_rate,
              dimension_numbers=dimension_numbers,
              feature_group_count=feature_group_count,
          )
      )
  
imports: import jax
  import numpy
  from jax import lax
  from jax import numpy
  from keras.backend import standardize_data_format
  from keras.backend import standardize_dtype
  from keras.backend.common.backend_utils import compute_conv_transpose_padding_args_for_jax
  from keras.backend.config import epsilon
  from keras.backend.numpy.core import cast
  from keras.backend.numpy.core import convert_to_tensor
  from keras.backend.numpy.core import is_tensor
  from keras.utils.module_utils import scipy
label_desc: Use image node_id 23 for calling a global function with example usage: depthwise_conv(inputs, kernel, strides, padding, data_format, dilation_rate) and returns: np