code: def forward(self, inputs):
      self._config_use_ideep = chainer.config.use_ideep
      if (
          intel64.should_use_ideep(">=auto")
          and self._w_dtype == numpy.float32
          and intel64.inputs_all_ready(inputs)
      ):
          # iDeep implementation
          return self._forward_ideep(inputs)
  
      # Generic implementation
      self.retain_inputs((0, 1))
      x, gy = inputs
  
      if (
          isinstance(gy, numpy.ndarray)
          and not (gy.flags.c_contiguous or gy.flags.f_contiguous)
          and 1 in gy.shape
      ):
          gy = numpy.ascontiguousarray(gy)
  
      gW = gy.T.dot(x).astype(self._w_dtype, copy=False)
      return (gW,)
  
imports: import numpy
  from chainer import backend
  from chainer.backends import intel64
  from chainer import function_node
  import chainer.functions
  from chainer.graph_optimizations import static_code
  from chainer import utils
  from chainer.utils import type_check
  import chainerx
label_desc: Use image node_id 2 for calling the LinearGradWeight obj's underlying member method code with example usage: obj.forward(inputs) and returns: gW, self