code: def conv_nd(
      model,
      blob_in,
      blob_out,
      dim_in,
      dim_out,
      kernel,
      weight_init=None,
      bias_init=None,
      WeightInitializer=None,
      BiasInitializer=None,
      group=1,
      transform_inputs=None,
      order="NCHW",
      **kwargs,
  ):
      """N-dimensional convolution for inputs with NCHW storage order."""
      assert order == "NCHW", "ConvNd only supported for NCHW storage."
      return _ConvBase(
          model,
          True,
          blob_in,
          blob_out,
          dim_in,
          dim_out,
          kernel,
          weight_init,
          bias_init,
          WeightInitializer,
          BiasInitializer,
          group,
          transform_inputs,
          order=order,
          **kwargs,
      )
  
file_path: pytorch/caffe2/python/helpers/conv.py
imports: from caffe2.python import core
  from caffe2.python.modeling import initializers
  from caffe2.python.modeling.parameter_info import ParameterTags
label_desc: Use image node_id 2 for calling a global function with example usage: conv_nd(model, blob_in, blob_out, dim_in, dim_out, kernel, weight_init, bias_init, WeightInitializer, BiasInitializer, group, transform_inputs, order) and returns: _ConvBase