class_docstr: Conditionally Parameterized Convolution
  
  Inspired by: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/condconv/condconv_layers.py
  Grouped convolution hackery for parallel execution of the per-sample kernel filters inspired by this discussion:
  https://github.com/pytorch/pytorch/issues/17983
  
  Args:
      in_channels (`int`):
          Number of channels in the input image
      out_channels (`int`):
          Number of channels produced by the convolution.
      kernel_size (`int`):
          Size of the convolving kernel.
      stride (`int`):
          Stride of the convolution.
      padding (`str`):
          Padding added to all four sides of the input.
      dilation (`int`):
          Spacing between kernel elements.
      groups (`int`):
          Number of blocked connections from input channels to output channels.
      bias (`bool`):
          If True, adds a learnable bias to the output.
      num_experts (`int`):
          The number of expert kernels and biases.
code: def reset_parameters(self) -> None:
      init_weight = get_condconv_initializer(
          partial(nn.init.kaiming_uniform_, a=math.sqrt(5)),
          self.num_experts,
          self.weight_shape,
      )
      init_weight(self.weight)
      if self.bias is not None:
          fan_in = np.prod(self.weight_shape[1:])
          bound = 1 / math.sqrt(fan_in)
          init_bias = get_condconv_initializer(
              partial(nn.init.uniform_, a=-bound, b=bound),
              self.num_experts,
              self.bias_shape,
          )
          init_bias(self.bias)
  
file_path: towhee/towhee/models/layers/cond_conv2d.py
imports: import math
  from typing import Callable, Tuple
  from functools import partial
  import numpy
  import torch
  from torch import nn
  from torch.nn import functional
  from towhee.models.utils.general_utils import to_2tuple
  from towhee.models.layers.conv2d_same import conv2d_same
  from towhee.models.layers.padding_functions import get_padding_value
label_desc: Use image node_id 2 for calling the CondConv2d obj's underlying member method code with example usage: obj.reset_parameters() without return types