code: def separable_conv(
      inputs,
      depthwise_kernel,
      pointwise_kernel,
      strides=1,
      padding="valid",
      data_format=None,
      dilation_rate=1,
  ):
      depthwise_conv_output = depthwise_conv(
          inputs,
          depthwise_kernel,
          strides,
          padding,
          data_format,
          dilation_rate,
      )
      return conv(
          depthwise_conv_output,
          pointwise_kernel,
          strides=1,
          padding="valid",
          data_format=data_format,
          dilation_rate=dilation_rate,
      )
  
imports: import torch
  import torch.nn.functional
  import tree
  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_torch
  from keras.backend.config import epsilon
  from keras.backend.torch.core import cast
  from keras.backend.torch.core import convert_to_tensor
  from keras.backend.torch.core import get_device
  from keras.backend.torch.numpy import expand_dims
  from keras.backend.torch.numpy import maximum
  from keras.backend.torch.numpy import where
  from keras.utils.argument_validation import standardize_tuple
label_desc: Use image node_id 26 for calling a global function with example usage: separable_conv(inputs, depthwise_kernel, pointwise_kernel, strides, padding, data_format, dilation_rate) and returns: conv