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Rename aesara to pytensor.
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‎examples/case_studies/GEV.ipynb

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"metadata": {},
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"outputs": [],
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"source": [
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"import aesara.tensor as at\n",
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"import arviz as az\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import pymc as pm\n",
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"import pymc_experimental.distributions as pmx\n",
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"import pytensor.tensor as at\n",
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"\n",
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"from arviz.plots import plot_utils as azpu"
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]
@@ -984,7 +984,7 @@
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"Python version : 3.10.6\n",
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"IPython version : 8.5.0\n",
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"\n",
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"aesara: 2.8.6\n",
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"pytensor: 2.8.6\n",
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"arviz : 0.12.1\n",
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"\n",
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"pymc_experimental: 0.0.1\n",
@@ -993,7 +993,7 @@
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"arviz : 0.12.1\n",
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"numpy : 1.23.3\n",
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"json : 2.0.9\n",
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"aesara : 2.8.6\n",
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"pytensor : 2.8.6\n",
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"pymc : 3.9.3+1493.g372d7c24\n",
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"\n",
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"Watermark: 2.3.1\n",
@@ -1003,7 +1003,7 @@
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],
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"source": [
10051005
"%load_ext watermark\n",
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"%watermark -n -u -v -iv -w -p aesara,arviz"
1006+
"%watermark -n -u -v -iv -w -p pytensor,arviz"
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]
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},
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{

‎examples/case_studies/LKJ.ipynb

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"Python version : 3.7.12\n",
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"IPython version : 5.5.0\n",
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"\n",
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"aesara: 2.4.0\n",
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"pytensor: 2.4.0\n",
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"xarray: 0.18.2\n",
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"\n",
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"pymc : 4.0.0b2\n",
@@ -1004,7 +1004,7 @@
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],
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"source": [
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"%load_ext watermark\n",
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"%watermark -n -u -v -iv -w -p aesara,xarray"
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"%watermark -n -u -v -iv -w -p pytensor,xarray"
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}
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],

‎examples/case_studies/bayesian_ab_testing_introduction.ipynb

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"Python version : 3.8.10\n",
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"IPython version : 8.4.0\n",
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"\n",
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"aesara: 2.7.3\n",
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"pytensor: 2.7.3\n",
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"xarray: 2022.3.0\n",
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"\n",
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"matplotlib: 3.5.2\n",
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],
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"source": [
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"%load_ext watermark\n",
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"%watermark -n -u -v -iv -w -p aesara,xarray"
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"%watermark -n -u -v -iv -w -p pytensor,xarray"
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‎examples/case_studies/binning.ipynb

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"We are now in a position to sketch out a generative PyMC model:\n",
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"\n",
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"```python\n",
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"import aesara.tensor as at\n",
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"import pytensor.tensor as at\n",
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"\n",
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"with pm.Model() as model:\n",
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" # priors\n",
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"source": [
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"import warnings\n",
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"\n",
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"import aesara.tensor as at\n",
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"import arviz as az\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import pymc as pm\n",
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"import pytensor.tensor as at\n",
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"import seaborn as sns\n",
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"\n",
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"warnings.filterwarnings(action=\"ignore\", category=UserWarning)"
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"pymc : 4.0.0b6\n",
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"arviz : 0.12.1\n",
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"numpy : 1.22.4\n",
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"aesara : 2.5.1\n",
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"pytensor : 2.5.1\n",
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"\n",
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"Watermark: 2.3.1\n",
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"\n"

‎examples/case_studies/blackbox_external_likelihood_numpy.ipynb

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‎examples/case_studies/factor_analysis.ipynb

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}
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],
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"source": [
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"import aesara.tensor as at\n",
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"import arviz as az\n",
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"import matplotlib\n",
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"import numpy as np\n",
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"import pymc as pm\n",
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"import pytensor.tensor as at\n",
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"import scipy as sp\n",
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"import seaborn as sns\n",
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"import xarray as xr\n",
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"text": [
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"Auto-assigning NUTS sampler...\n",
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"Initializing NUTS using jitter+adapt_diag...\n",
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"/home/oriol/miniconda3/envs/arviz/lib/python3.9/site-packages/pymc/aesaraf.py:1005: UserWarning: The parameter 'updates' of aesara.function() expects an OrderedDict, got <class 'dict'>. Using a standard dictionary here results in non-deterministic behavior. You should use an OrderedDict if you are using Python 2.7 (collections.OrderedDict for older python), or use a list of (shared, update) pairs. Do not just convert your dictionary to this type before the call as the conversion will still be non-deterministic.\n",
209-
" aesara_function = aesara.function(\n",
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"/home/oriol/miniconda3/envs/arviz/lib/python3.9/site-packages/pymc/pytensorf.py:1005: UserWarning: The parameter 'updates' of pytensor.function() expects an OrderedDict, got <class 'dict'>. Using a standard dictionary here results in non-deterministic behavior. You should use an OrderedDict if you are using Python 2.7 (collections.OrderedDict for older python), or use a list of (shared, update) pairs. Do not just convert your dictionary to this type before the call as the conversion will still be non-deterministic.\n",
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" pytensor_function = pytensor.function(\n",
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"metadata": {},
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"outputs": [],
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"source": [
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"def expand_packed_block_triangular(d, k, packed, diag=None, mtype=\"aesara\"):\n",
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"def expand_packed_block_triangular(d, k, packed, diag=None, mtype=\"pytensor\"):\n",
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" # like expand_packed_triangular, but with d > k.\n",
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" assert mtype in {\"aesara\", \"numpy\"}\n",
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" assert mtype in {\"pytensor\", \"numpy\"}\n",
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" assert d >= k\n",
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" def set_(M, i_, v_):\n",
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" if mtype == \"aesara\":\n",
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" if mtype == \"pytensor\":\n",
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" return at.set_subtensor(M[i_], v_)\n",
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" M[i_] = v_\n",
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" return M\n",
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"\n",
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" out = at.zeros((d, k), dtype=float) if mtype == \"aesara\" else np.zeros((d, k), dtype=float)\n",
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" out = at.zeros((d, k), dtype=float) if mtype == \"pytensor\" else np.zeros((d, k), dtype=float)\n",
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" if diag is None:\n",
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" idxs = np.tril_indices(d, m=k)\n",
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" out = set_(out, idxs, packed)\n",
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"text": [
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"/home/oriol/miniconda3/envs/arviz/lib/python3.9/site-packages/pymc/aesaraf.py:1005: UserWarning: The parameter 'updates' of aesara.function() expects an OrderedDict, got <class 'dict'>. Using a standard dictionary here results in non-deterministic behavior. You should use an OrderedDict if you are using Python 2.7 (collections.OrderedDict for older python), or use a list of (shared, update) pairs. Do not just convert your dictionary to this type before the call as the conversion will still be non-deterministic.\n",
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" aesara_function = aesara.function(\n",
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"/home/oriol/miniconda3/envs/arviz/lib/python3.9/site-packages/pymc/pytensorf.py:1005: UserWarning: The parameter 'updates' of pytensor.function() expects an OrderedDict, got <class 'dict'>. Using a standard dictionary here results in non-deterministic behavior. You should use an OrderedDict if you are using Python 2.7 (collections.OrderedDict for older python), or use a list of (shared, update) pairs. Do not just convert your dictionary to this type before the call as the conversion will still be non-deterministic.\n",
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" pytensor_function = pytensor.function(\n",
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"Multiprocess sampling (4 chains in 4 jobs)\n",
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"NUTS: [W_z, W_b, F, psi]\n"
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"/home/oriol/miniconda3/envs/arviz/lib/python3.9/site-packages/pymc/aesaraf.py:1005: UserWarning: The parameter 'updates' of aesara.function() expects an OrderedDict, got <class 'dict'>. Using a standard dictionary here results in non-deterministic behavior. You should use an OrderedDict if you are using Python 2.7 (collections.OrderedDict for older python), or use a list of (shared, update) pairs. Do not just convert your dictionary to this type before the call as the conversion will still be non-deterministic.\n",
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" aesara_function = aesara.function(\n"
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"/home/oriol/miniconda3/envs/arviz/lib/python3.9/site-packages/pymc/pytensorf.py:1005: UserWarning: The parameter 'updates' of pytensor.function() expects an OrderedDict, got <class 'dict'>. Using a standard dictionary here results in non-deterministic behavior. You should use an OrderedDict if you are using Python 2.7 (collections.OrderedDict for older python), or use a list of (shared, update) pairs. Do not just convert your dictionary to this type before the call as the conversion will still be non-deterministic.\n",
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" pytensor_function = pytensor.function(\n"
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{
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"arviz : 0.12.0\n",
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"pytensor : 2.5.1\n",
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‎examples/case_studies/hierarchical_partial_pooling.ipynb

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"pytensor : 2.7.5\n",
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‎examples/case_studies/item_response_nba.ipynb

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‎examples/case_studies/mediation_analysis.ipynb

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‎examples/case_studies/moderation_analysis.ipynb

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‎examples/case_studies/multilevel_modeling.ipynb

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‎examples/case_studies/probabilistic_matrix_factorization.ipynb

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"/Users/severinhatt/miniconda3/envs/pymc-hack/lib/python3.9/site-packages/pymc/aesaraf.py:1005: UserWarning: The parameter 'updates' of aesara.function() expects an OrderedDict, got <class 'dict'>. Using a standard dictionary here results in non-deterministic behavior. You should use an OrderedDict if you are using Python 2.7 (collections.OrderedDict for older python), or use a list of (shared, update) pairs. Do not just convert your dictionary to this type before the call as the conversion will still be non-deterministic.\n",
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"/Users/severinhatt/miniconda3/envs/pymc-hack/lib/python3.9/site-packages/pymc/pytensorf.py:1005: UserWarning: The parameter 'updates' of pytensor.function() expects an OrderedDict, got <class 'dict'>. Using a standard dictionary here results in non-deterministic behavior. You should use an OrderedDict if you are using Python 2.7 (collections.OrderedDict for older python), or use a list of (shared, update) pairs. Do not just convert your dictionary to this type before the call as the conversion will still be non-deterministic.\n",
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"%watermark -n -u -v -iv -w -p pytensor,aeppl,xarray"
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{

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