code: def test_forward_backward_1d(
      inshape,
      kernel,
      pad,
      stride,
      dilation,
      with_bias,
      multiplier,
      seed,
      ctx,
      func_name,
  ):
      base_axis = len(inshape) - 2
      sample_channels = inshape[base_axis]
      outmap_channels = sample_channels * multiplier
      rng = np.random.RandomState(seed)
      x = rng.randn(*inshape).astype(np.float32)
      w = rng.randn(*((outmap_channels,) + kernel)).astype(np.float32)
      b = (
          rng.randn(outmap_channels).astype(np.float32)
          if with_bias
          else None
      )
      inputs = [x, w, b]
      func_args = [base_axis, pad, stride, dilation, multiplier]
      reference = ref_depthwise_convolution_1d
      function_tester(
          rng,
          F.depthwise_convolution,
          reference,
          inputs,
          func_args,
          atol_f=1e-4,
          atol_b=3e-3,
          dstep=1e-2,
          ctx=ctx,
          func_name=func_name,
      )
  
file_path: nnabla/python/test/function/test_depthwise_convolution.py
imports: from __future__ import division
  import pytest
  import numpy
  import nnabla
  import nnabla.functions
  import nnabla.parametric_functions
  import refs
  from nbla_test_utils import list_context
  from nbla_test_utils import function_tester
label_desc: Use image node_id 4 for calling a global function with example usage: test_forward_backward_1d(inshape, kernel, pad, stride, dilation, with_bias, multiplier, seed, ctx, func_name) without return types