code: def test_initializer_variance(
      self,
      num_spatial_dims,
      kernel_shape,
      in_channels,
      output_channels,
      data_format,
  ):
      c = conv.ConvNDTranspose(
          num_spatial_dims=num_spatial_dims,
          kernel_shape=kernel_shape,
          output_channels=output_channels,
          data_format=data_format,
      )
  
      inputs = jnp.ones(
          [16] + ([32] * num_spatial_dims) + [in_channels]
      )
      c(inputs)
  
      w = c.params_dict()["conv_nd_transpose/w"]
      actual_std = w.std()
      expected_std = 1 / (
          np.sqrt(np.prod(kernel_shape + (in_channels,)))
      )
  
      # This ratio of the error compared to the expected std might be somewhere
      # around 0.15 normally. We check it is not > 0.5, as that would indicate
      # something seriously wrong (ie the previous buggy initialization).
      rel_diff = np.abs(actual_std - expected_std) / expected_std
      self.assertLess(rel_diff, 0.5)
  
imports: from absl.testing import absltest
  from absl.testing import parameterized
  from haiku._src import conv
  from haiku._src import initializers
  from haiku._src import test_utils
  from haiku._src import transform
  import jax
  from jax import random
  import jax.numpy
  import numpy
label_desc: Use image node_id 10 for calling the ConvTransposeTest obj's underlying member method code with example usage: obj.test_initializer_variance(num_spatial_dims, kernel_shape, in_channels, output_channels, data_format) without return types