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[
    {
        "instruments": [
            {
                "name": "Acoustic Grand Piano",
                "program": 1
            },
            {
                "name": "Bright Acoustic Piano",
                "program": 2

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1 n02119789 kit fox, Vulpes macrotis
2 n02100735 English setter
3 n02096294 Australian terrier
4 n02066245 grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus
5 n02509815 lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens
6 n02124075 Egyptian cat
7 n02417914 ibex, Capra ibex
8 n02123394 Persian cat
9 n02125311 cougar, puma, catamount, mountain lion, painter, panther, Felis concolor
10 n02423022 gazelle

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import torch
import torch.distributed as dist
import torch.multiprocessing as mp

from torch.utils.data import IterableDataset, DataLoader


class DistributedIterableDataset(IterableDataset):
    """
    Example implementation of an IterableDataset that handles both multiprocessing (num_workers > 0) 

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"""
Sample code for residue coloring in a dash-bio 3d molecule view

Requirements:
- dash
- dash-bio
- numpy
- biopython

pip install dash dash-bio numpy biopython

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import time
import torch
import torch.nn as nn
import torch.nn.functional as F


bz = 128
seq_len = 512
d_model = 64
n_heads = 8

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import torch.nn as nn
import torch.nn.functional as F


class FlashAttentionTransformerEncoder(nn.Module):
    def __init__(
        self,
        dim_model,
        num_layers,
        num_heads=None,

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import torch
import nibabel as nib
import numpy as np
from tensordidct import TensorDict, MemmapTensor

files_by_modality = {
    'flair': [...]
}

num_files = ...

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