I have the following ‘Linear’ function:
import math
import torch
from torch.nn.parameter import Parameter
from … import functional as F
from … import init
from .module import Module
from …_jit_internal import weak_module, weak_script_method
@weak_module
class Linear(Module):
r"""Applies a linear transformation to the incoming data: :math:y = xA^T + b
Args:
    in_features: size of each input sample
    out_features: size of each output sample
    bias: If set to False, the layer will not learn an additive bias.
        Default: ``True``

Shape:
    - Input: :math:`(N, *, \text{in\_features})` where :math:`*` means any number of
      additional dimensions
    - Output: :math:`(N, *, \text{out\_features})` where all but the last dimension
      are the same shape as the input.

Attributes:
    weight: the learnable weights of the module of shape
        :math:`(\text{out\_features}, \text{in\_features})`. The values are
        initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where
        :math:`k = \frac{1}{\text{in\_features}}`
    bias:   the learnable bias of the module of shape :math:`(\text{out\_features})`.
            If :attr:`bias` is ``True``, the values are initialized from
            :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
            :math:`k = \frac{1}{\text{in\_features}}`

Examples::

    >>> m = nn.Linear(20, 30)
    >>> input = torch.randn(128, 20)
    >>> output = m(input)
    >>> print(output.size())
    torch.Size([128, 30])
"""
__constants__ = ['bias']

def __init__(self, in_features, out_features, bias=True):
    super(Linear, self).__init__()
    self.in_features = in_features
    self.out_features = out_features
    self.weight = Parameter(torch.Tensor(out_features, in_features))
    if bias:
        self.bias = Parameter(torch.Tensor(out_features))
    else:
        self.register_parameter('bias', None)
    self.reset_parameters()

def reset_parameters(self):
    init.kaiming_uniform_(self.weight, a=math.sqrt(5))
    if self.bias is not None:
        fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
        bound = 1 / math.sqrt(fan_in)
        init.uniform_(self.bias, -bound, bound)

@weak_script_method
def forward(self, input):
    return F.linear(input, self.weight, self.bias)

def extra_repr(self):
    return 'in_features={}, out_features={}, bias={}'.format(
        self.in_features, self.out_features, self.bias is not None
    )

@weak_module
class Bilinear(Module):
r"""Applies a bilinear transformation to the incoming data:
:math:y = x_1 A x_2 + b
Args:
    in1_features: size of each first input sample
    in2_features: size of each second input sample
    out_features: size of each output sample
    bias: If set to False, the layer will not learn an additive bias.
        Default: ``True``

Shape:
    - Input: :math:`(N, *, \text{in1\_features})`, :math:`(N, *, \text{in2\_features})`
      where :math:`*` means any number of additional dimensions. All but the last
      dimension of the inputs should be the same.
    - Output: :math:`(N, *, \text{out\_features})` where all but the last dimension
      are the same shape as the input.

Attributes:
    weight: the learnable weights of the module of shape
        :math:`(\text{out\_features} x \text{in1\_features} x \text{in2\_features})`.
        The values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where
        :math:`k = \frac{1}{\text{in1\_features}}`
    bias:   the learnable bias of the module of shape :math:`(\text{out\_features})`
            If :attr:`bias` is ``True``, the values are initialized from
            :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where
            :math:`k = \frac{1}{\text{in1\_features}}`

Examples::

    >>> m = nn.Bilinear(20, 30, 40)
    >>> input1 = torch.randn(128, 20)
    >>> input2 = torch.randn(128, 30)
    >>> output = m(input1, input2)
    >>> print(output.size())
    torch.Size([128, 40])
"""
__constants__ = ['in1_features', 'in2_features', 'out_features', 'bias']

def __init__(self, in1_features, in2_features, out_features, bias=True):
    super(Bilinear, self).__init__()
    self.in1_features = in1_features
    self.in2_features = in2_features
    self.out_features = out_features
    self.weight = Parameter(torch.Tensor(out_features, in1_features, in2_features))

    if bias:
        self.bias = Parameter(torch.Tensor(out_features))
    else:
        self.register_parameter('bias', None)
    self.reset_parameters()

def reset_parameters(self):
    bound = 1 / math.sqrt(self.weight.size(1))
    init.uniform_(self.weight, -bound, bound)
    if self.bias is not None:
        init.uniform_(self.bias, -bound, bound)

@weak_script_method
def forward(self, input1, input2):
    return F.bilinear(input1, input2, self.weight, self.bias)

def extra_repr(self):
    return 'in1_features={}, in2_features={}, out_features={}, bias={}'.format(
        self.in1_features, self.in2_features, self.out_features, self.bias is not None
    )

TODO: PartialLinear - maybe in sparse?
But the link (as mentioned in the previous post) discussed about the ‘LinearFunction’ which is not present in the torch.nn.