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class BFS(nn.Module):
    def __init__(self):
        super().__init__()
        # convolutional kernel to connect with neighbors on the grid
        self.step = nn.Conv2d(1, 1, kernel_size=3, padding=1, bias=False)
        self.step.weight.data = 0.3 * torch.tensor([[1, 1, 1],
                                                    [1, 2, 1],
                                                    [1, 1, 1]]).view(1,1,3,3)
        self.steps = 4

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# prior - likelihood conflict

library(rethinking)

yobs <- 0

mtt <- ulam(
    alist(
        y ~ dstudent(2,mu,1),
        mu ~ dstudent(2,10,1)

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"Two independent linear maps using grouped Conv1d"
import torch
import torch.nn as nn


class DualLinearSerial(nn.Module):
    def __init__(self, in_channels, out_channels, bias=True):
        super().__init__()
        self.left = nn.Linear(in_channels // 2, out_channels // 2, bias=bias)
        self.right = nn.Linear(in_channels // 2, out_channels // 2, bias=bias)

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diff --git a/cuda/practicals/tree-ex1/findneighbors.hpp b/cuda/practicals/tree-ex1/findneighbors.hpp
index 7ba7329..412e6cd 100644
--- a/cuda/practicals/tree-ex1/findneighbors.hpp
+++ b/cuda/practicals/tree-ex1/findneighbors.hpp
@@ -16,7 +16,7 @@ namespace cstone
 
 //! @brief generic depth-first traversal of an octree that works on CPU and GPU with customizable descent criteria
 template<class C, class A>
-void depthFirstTraversal(const TreeNodeIndex* childOffsets, C&& continuationCriterion, A&& endpointAction)
+__host__ __device__ void depthFirstTraversal(const TreeNodeIndex* childOffsets, C&& continuationCriterion, A&& endpointAction)

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import torch
import torch.nn as nn
import math

input_dim = 80
head_dim = 64
heads = 12
p_drop = 0.1
layers = 6
context = 128

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import argparse
from pathlib import Path
from hashlib import sha1
import math

import torch

parser = argparse.ArgumentParser(description='compute nll/bpc/bpb on a text dataset')
parser.add_argument('--device', type=str, default='cuda:0')
parser.add_argument('ckpt_path')

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import numpy as np

def rgb_to_hue(image):
  """Compute hue value from BGR image.

  Uses Hue definition from https://mattlockyer.github.io/iat455/documents/rgb-hsv.pdf
  """
  image = image / 255
  m_max = np.max(image, axis=-1) # (W,H,C) -> (W,H)
  m_min = np.min(image, axis=-1) # (W,H,C) -> (W,H)