Change:
why 1.21.6? because that's the oldest supported numpy on windows/python3.10: all cc
Here is the test for pinning numpy to 1.21.6 The reason its was not pinned to this version is that conda can't properly resolve all the dependencies at this step here:
It looks like you are correct, I will put up a PR to pin lower then already pinned:
Here is the PR:

Discussion:
:members: :hidden:`InstanceNorm1d` <del> ~~~~~~~~~~~~~~~~~~~~~ </del> <ins> ~~~~~~~~~~~~~~~~~~~~~~~~ </ins> .. autoclass:: InstanceNorm1d :members: :hidden:`InstanceNorm2d` <del> ~~~~~~~~~~~~~~~~~~~~~ </del> <ins> ~~~~~~~~~~~~~~~~~~~~~~~~ </ins> .. autoclass:: InstanceNorm2d :members: :hidden:`InstanceNorm3d` <del> ~~~~~~~~~~~~~~~~~~~~~ </del> <ins> ~~~~~~~~~~~~~~~~~~~~~~~~ </ins> .. autoclass:: InstanceNorm3d :members:

def calculate_gain(nonlinearity, param=None): """Return the recommended gain value for the given nonlinearity function. The values are as follows: ============ ========================================== nonlinearity gain ============ ==========================================

"""Fills the input Tensor or Variable with values according to the method described in "Understanding the difficulty of training deep feedforward neural networks" - Glorot, X. & Bengio, Y. (2010), using a uniform distribution. The resulting tensor will have values sampled from :math:`U(-a, a)` where <del> :math:`a = gain times sqrt{2 / (fan_in + fan_out)} times sqrt{3}`. Also known as Glorot initialisation. </del> <ins> :math:`a = gain times sqrt{2 / (fan_in + fan_out)} times sqrt{3}`. Also known as Glorot initialisation. </ins> Args: tensor: an n-dimensional torch.Tensor or autograd.Variable

"""Fills the input Tensor or Variable with values according to the method described in "Understanding the difficulty of training deep feedforward neural networks" - Glorot, X. & Bengio, Y. (2010), using a normal distribution. The resulting tensor will have values sampled from :math:`N(0, std)` where <del> :math:`std = gain times sqrt{2 / (fan_in + fan_out)}`. Also known as Glorot initialisation. </del> <ins> :math:`std = gain times sqrt{2 / (fan_in + fan_out)}`. Also known as Glorot initialisation. </ins> Args: tensor: an n-dimensional torch.Tensor or autograd.Variable

"""Fills the input Tensor or Variable with values according to the method described in "Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification" - He, K. et al. (2015), using a uniform distribution. The resulting tensor will have values sampled from :math:`U(-bound, bound)` where <del> :math:`bound = sqrt{2 / ((1 + a^2) times fan_in)} times sqrt{3}`. Also known as He initialisation. </del> <ins> :math:`bound = sqrt{2 / ((1 + a^2) times fan_in)} times sqrt{3}`. Also known as He initialisation. </ins> Args: tensor: an n-dimensional torch.Tensor or autograd.Variable

"""Fills the input Tensor or Variable with values according to the method described in "Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification" - He, K. et al. (2015), using a normal distribution. The resulting tensor will have values sampled from :math:`N(0, std)` where <del> :math:`std = sqrt{2 / ((1 + a^2) times fan_in)}`. Also known as He initialisation. </del> <ins> :math:`std = sqrt{2 / ((1 + a^2) times fan_in)}`. Also known as He initialisation. </ins> Args: tensor: an n-dimensional torch.Tensor or autograd.Variable

class InstanceNorm2d(_InstanceNorm): r"""Applies Instance Normalization over a 4d input that is seen as a mini-batch of 3d inputs .. math:: y = frac{x - mean[x]}{ sqrt{Var[x]} + epsilon} * gamma + beta The mean and standard-deviation are calculated per-dimension separately for each object in a mini-batch. Gamma and beta are learnable parameter vectors of size C (where C is the input size).