code: def static_linear_no_bias(self, xp, optimized, inputs, outputs):
      x, W = inputs
      y = outputs[0]
      # NumPy raises an error when the array is not contiguous.
      # See: https://github.com/chainer/chainer/issues/2744
      # TODO(niboshi): Remove this code when NumPy is fixed.
      if (
          isinstance(x, numpy.ndarray)
          and not (x.flags.c_contiguous or x.flags.f_contiguous)
          and 1 in x.shape
      ):
          x = numpy.ascontiguousarray(x)
  
      if optimized:
          # Note: We can only call this function when both x and W
          # have the same dtype. Otherwise, the output type (for y)
          # may not be as expected (i.e., not the same dtype as x).
          xp.dot(x, W.T, out=y)
      else:
          y[:] = x.dot(W.T).astype(x.dtype, copy=False)
  
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 LinearFunction obj's underlying member method code with example usage: obj.static_linear_no_bias(xp, optimized, inputs, outputs) without return types