class_docstr: A helper class for handling relay functions from darknet model.
docstr: Get the convolution layer weights and biases.
docstr_tok: ["Get","the","convolution","layer","weights","and","biases","."]
code: def _get_convolution_weights(self, layer, opname):
      """Get the convolution layer weights and biases."""
      if layer.nweights == 0:
          return None
  
      if (
          layer.n * layer.c // layer.groups * layer.size * layer.size
      ) != layer.nweights:
          raise RuntimeError(
              "layer weights size not matching with n c h w"
          )
  
      params = {}
      shape = (layer.n, layer.c // layer.groups, layer.size, layer.size)
      weights = self._read_memory_buffer(shape, layer.weights)
  
      biases = self._read_memory_buffer((layer.n,), layer.biases)
  
      k = _get_params_name(opname, "weight")
      params[k] = tvm.nd.array(weights)
  
      if layer.batch_normalize == 1 and layer.dontloadscales != 1:
          params.update(
              self._get_batchnorm_weights(layer, opname, layer.n)
          )
          k = _get_params_name(opname, "beta")
          params[k] = tvm.nd.array(biases)
      else:
          k = _get_params_name(opname, "bias")
          params[k] = tvm.nd.array(biases)
      return params
  
imports: from enum import Enum
  import numpy
  import tvm
  from tvm.ir import IRModule
  from ..None import analysis
  from ..None import expr
  from ..None import function
  from .common import get_relay_op, new_var
label_desc: Use image node_id 3 for calling the GraphProto obj's underlying member method code with example usage: obj._get_convolution_weights(layer, opname) and returns: params, None