code: def affine(
      inp,
      n_outmaps,
      base_axis=1,
      w_init=None,
      b_init=None,
      fix_parameters=False,
      rng=None,
      with_bias=True,
      apply_w=None,
      apply_b=None,
  ):
      """
      The affine layer, also known as the fully connected layer. Computes
  
      .. math::
          {\\mathbf y} = {\\mathbf A} {\\mathbf x} + {\\mathbf b}.
  
      where :math:`{\\mathbf x}, {\\mathbf y}` are the inputs and outputs respectively,
      and :math:`{\\mathbf A}, {\\mathbf b}` are constants.
  
      Args:
          inp (~nnabla.Variable): Input N-D array with shape (:math:`M_0 \\times \ldots \\times M_{B-1} \\times D_B \\times \ldots \\times D_N`). Dimensions before and after base_axis are flattened as if it is a matrix.
          n_outmaps (:obj:`int` or :obj:`tuple` of :obj:`int`): Number of output neurons per data.
          base_axis (int): Dimensions up to `base_axis` are treated as the sample dimensions.
          w_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`): Initializer for weight. By default, it is initialized with :obj:`nnabla.initializer.UniformInitializer` within the range determined by :obj:`nnabla.initializer.calc_uniform_lim_glorot`.
          b_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`): Initializer for bias. By default, it is initialized with zeros if `with_bias` is `True`.
          fix_parameters (bool): When set to `True`, the weights and biases will not be updated.
          rng (numpy.random.RandomState): Random generator for Initializer.
          with_bias (bool): Specify whether to include the bias term.
          apply_w (function): Lambda, function, or callable object applied to the weights.
          apply_b (function): Lambda, function, or callable object applied to the bias.
  
      Returns:
          :class:`~nnabla.Variable`: :math:`(B + 1)`-D array. (:math:`M_0 \\times \ldots \\times M_{B-1} \\times L`)
  
      """
      if not hasattr(n_outmaps, "__iter__"):
          n_outmaps = [n_outmaps]
      n_outmaps = list(n_outmaps)
      n_outmap = int(np.prod(n_outmaps))
      if w_init is None:
          inmaps = np.prod(inp.shape[base_axis:])
          w_init = UniformInitializer(
              calc_uniform_lim_glorot(inmaps, n_outmap), rng=rng
          )
      if with_bias and b_init is None:
          b_init = ConstantInitializer()
      w = get_parameter_or_create(
          "W",
          [int(np.prod(inp.shape[base_axis:]))] + n_outmaps,
          w_init,
          True,
          not fix_parameters,
      )
      if apply_w is not None:
          w = apply_w(w)
      b = None
      if with_bias:
          b = get_parameter_or_create(
              "b", n_outmaps, b_init, True, not fix_parameters
          )
          if apply_b is not None:
              b = apply_b(b)
      return F.affine(inp, w, b, base_axis)
  
imports: from six import exec_
  import numpy
  import nnabla
  import nnabla.functions
  from nnabla.parameter import get_parameter_or_create, get_parameter
  from nnabla.initializer import calc_uniform_lim_glorot, ConstantInitializer, NormalInitializer, UniformInitializer, WeightNormalizationScaleInitializer
label_desc: Use image node_id 2 for calling a global function with example usage: affine(inp, n_outmaps, base_axis, w_init, b_init, fix_parameters, rng, with_bias, apply_w, apply_b) and returns: F