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        "author": "Wessel Bruinsma",
        "author_email": "wessel.p.bruinsma@gmail.com",
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        "description": "# [LAB](http://github.com/wesselb/lab)\n\n[![CI](https://github.com/wesselb/lab/workflows/CI/badge.svg?branch=master)](https://github.com/wesselb/lab/actions?query=workflow%3ACI)\n[![Coverage Status](https://coveralls.io/repos/github/wesselb/lab/badge.svg?branch=master&service=github)](https://coveralls.io/github/wesselb/lab?branch=master)\n[![Latest Docs](https://img.shields.io/badge/docs-latest-blue.svg)](https://wesselb.github.io/lab)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n\nA generic interface for linear algebra backends: code it once, run it on any \nbackend\n\n* [Requirements and Installation](#requirements-and-installation)\n* [Basic Usage](#basic-usage)\n* [List of Types](#list-of-types)\n    - [General](#general)\n    - [NumPy](#numpy)\n    - [AutoGrad](#autograd)\n    - [TensorFlow](#tensorflow)\n    - [PyTorch](#pytorch)\n    - [JAX](#jax)\n* [List of Methods](#list-of-methods)\n    - [Constants](#constants)\n    - [Generic](#generic)\n    - [Linear Algebra](#linear-algebra)\n    - [Random](#random)\n    - [Shaping](#shaping)\n* [Devices](#devices)\n* [Lazy Shapes](#lazy-shapes)\n* [Random Numbers](#random-numbers)\n* [Control Flow Cache](#control-flow-cache)\n\n## Requirements and Installation\n\nSee [the instructions here](https://gist.github.com/wesselb/4b44bf87f3789425f96e26c4308d0adc).\nThen simply\n\n```\npip install backends\n```\n\n## Basic Usage\n\nThe basic use case for the package is to write code that automatically \ndetermines the backend to use depending on the types of its arguments.\n\nExample:\n\n```python\nimport lab as B\nimport lab.autograd    # Load the AutoGrad extension.\nimport lab.torch       # Load the PyTorch extension.\nimport lab.tensorflow  # Load the TensorFlow extension.\nimport lab.jax         # Load the JAX extension.\n\n\ndef objective(matrix):\n    outer_product = B.matmul(matrix, matrix, tr_b=True)\n    return B.mean(outer_product)\n```\n\nThe AutoGrad, PyTorch, TensorFlow, and JAX extensions are not loaded automatically to\nnot enforce a dependency on all three frameworks.\nAn extension can alternatively be loaded via `import lab.autograd as B`.\n\nRun it with NumPy and AutoGrad:\n\n```python\n>>> import autograd.numpy as np\n\n>>> objective(B.randn(np.float64, 2, 2))\n0.15772589216756833\n\n>>> grad(objective)(B.randn(np.float64, 2, 2))\narray([[ 0.23519042, -1.06282928],\n       [ 0.23519042, -1.06282928]])\n```\n\nRun it with TensorFlow:\n```python\n>>> import tensorflow as tf\n\n>>> objective(B.randn(tf.float64, 2, 2))\n<tf.Tensor 'Mean:0' shape=() dtype=float64>\n```\n\nRun it with PyTorch:\n```python\n>>> import torch\n\n>>> objective(B.randn(torch.float64, 2, 2))\ntensor(1.9557, dtype=torch.float64)\n```\n\nRun it with JAX:\n```python\n>>> import jax\n\n>>> import jax.numpy as jnp\n\n>>> jax.jit(objective)(B.randn(jnp.float32, 2, 2))\nDeviceArray(0.3109299, dtype=float32)\n\n>>> jax.jit(jax.grad(objective))(B.randn(jnp.float32, 2, 2))\nDeviceArray([[ 0.2525182, -1.26065  ],\n             [ 0.2525182, -1.26065  ]], dtype=float32)\n```\n\n## List of Types\nThis section lists all available types, which can be used to check types of \nobjects or extend functions.\n\nExample:\n\n```python\n>>> import lab as B\n\n>>> from plum import List, Tuple\n\n>>> import numpy as np\n\n>>> isinstance([1., np.array([1., 2.])], List(B.NPNumeric))\nTrue\n\n>>> isinstance([1., np.array([1., 2.])], List(B.TFNumeric))\nFalse\n\n>>> import tensorflow as tf\n\n>>> import lab.tensorflow\n\n>>> isinstance((tf.constant(1.), tf.ones(5)), Tuple(B.TFNumeric))\nTrue\n```\n\n### General\n\n```\nInt          # Integers\nFloat        # Floating-point numbers\nComplex      # Complex numbers\nBool         # Booleans\nNumber       # Numbers\nNumeric      # Numerical objects, including booleans\nDType        # Data type\nFramework    # Anything accepted by supported frameworks\nDevice       # Any device type\n```\n\n### NumPy\n\n```\nNPNumeric\nNPDType\nNPRandomState\n \nNP  # Anything NumPy\n```\n\n### AutoGrad\n\n```\nAGNumeric\nAGDType\nAGRandomState\n \nAG  # Anything AutoGrad\n```\n\n### TensorFlow\n\n```\nTFNumeric\nTFDType\nTFRandomState\nTFDevice\n \nTF  # Anything TensorFlow\n```\n\n\n### PyTorch\n\n```\nTorchNumeric\nTorchDType\nTorchDevice\nTorchRandomState\n \nTorch  # Anything PyTorch\n```\n\n\n### JAX\n\n```\nJAXNumeric\nJAXDType\nJAXDevice\nJAXRandomState\n \nJAX  # Anything JAX\n```\n\n\n## List of Methods\nThis section lists all available constants and methods.\n\n*\n    Arguments *must* be given as arguments and keyword arguments *must* be \n    given as keyword arguments.\n    For example, `sum(tensor, axis=1)` is valid, but `sum(tensor, 1)` is not.\n    \n* The names of arguments are indicative of their function:\n    - `a`, `b`, and `c` indicate general tensors.\n    -\n        `dtype` indicates a data type. E.g, `np.float32` or `tf.float64`; and\n        `rand(np.float32)` creates a NumPy random number, whereas\n        `rand(tf.float64)` creates a TensorFlow random number.\n        Data types are always given as the first argument.\n    -\n        `shape` indicates a shape.\n        The dimensions of a shape are always given as separate arguments to \n        the function.\n        E.g., `reshape(tensor, 2, 2)` is valid, but `reshape(tensor, (2, 2))`\n        is not.\n    -\n        `axis` indicates an axis over which the function may perform its action.\n        An axis is always given as a keyword argument.\n    -\n        `device` refers to a device on which a tensor can placed, which can\n        either be a framework-specific type or a string, e.g. `\"cpu\"`.\n    -\n        `ref` indicates a *reference tensor* from which properties, like its\n        shape and data type, will be used. E.g., `zeros(tensor)` creates a \n        tensor full of zeros of the same shape and data type as `tensor`.\n    \nSee the documentation for more detailed descriptions of each function. \n\n### Special Variables\n```\ndefault_dtype          # Default data type.\nepsilon                # Magnitude of diagonal to regularise matrices with.\ncholesky_retry_factor  # Retry the Cholesky, increasing `epsilon` by a factor at most this.\n```\n\n### Constants\n```\nnan\npi\nlog_2_pi\n```\n\n### Data Types\n```\ndtype(a)\ndtype_float(dtype)\ndtype_float(a)\ndtype_int(dtype)\ndtype_int(a)\n\npromote_dtypes(*dtype)\nissubdtype(dtype1, dtype2)\n```\n\n### Generic\n```\nisabstract(a)\njit(f, **kw_args)\n\nisnan(a)\nreal(a)\nimag(a)\n\ndevice(a)\non_device(device)\non_device(a)\nset_global_device(device)\nto_active_device(a)\n\nzeros(dtype, *shape)\nzeros(*shape)\nzeros(ref)\n\nones(dtype, *shape)\nones(*shape)\nones(ref)\n\nzero(dtype)\nzero(*refs)\n\none(dtype)\none(*refs)\n\neye(dtype, *shape)\neye(*shape)\neye(ref)\n\nlinspace(dtype, a, b, num)\nlinspace(a, b, num)\n\nrange(dtype, start, stop, step)\nrange(dtype, stop)\nrange(dtype, start, stop)\nrange(start, stop, step)\nrange(start, stop)\nrange(stop)\n\ncast(dtype, a)\n\nidentity(a)\nround(a)\nfloor(a)\nceil(a)\nnegative(a)\nabs(a)\nsign(a)\nsqrt(a)\nexp(a)\nlog(a)\nlog1p(a)\nsin(a)\narcsin(a)\ncos(a)\narccos(a)\ntan(a)\narctan(a)\ntanh(a)\narctanh(a)\nerf(a)\nsigmoid(a)\nsoftplus(a)\nrelu(a)\n\nadd(a, b)\nsubtract(a, b)\nmultiply(a, b)\ndivide(a, b)\npower(a, b)\nminimum(a, b)\nmaximum(a, b)\nleaky_relu(a, alpha)\n\nsoftmax(a, axis=None)\n\nmin(a, axis=None, squeeze=True)\nmax(a, axis=None, squeeze=True)\nsum(a, axis=None, squeeze=True)\nprod(a, axis=None, squeeze=True)\nmean(a, axis=None, squeeze=True)\nstd(a, axis=None, squeeze=True)\nlogsumexp(a, axis=None, squeeze=True)\nall(a, axis=None, squeeze=True)\nany(a, axis=None, squeeze=True)\n\nnansum(a, axis=None, squeeze=True)\nnanprod(a, axis=None, squeeze=True)\nnanmean(a, axis=None, squeeze=True)\nnanstd(a, axis=None, squeeze=True)\n\nargmin(a, axis=None)\nargmax(a, axis=None)\n\nlt(a, b)\nle(a, b)\ngt(a, b)\nge(a, b)\neq(a, b)\nne(a, b)\n\nbvn_cdf(a, b, c)\n\ncond(condition, f_true, f_false, xs**)\nwhere(condition, a, b)\nscan(f, xs, *init_state)\n\nsort(a, axis=-1, descending=False)\nargsort(a, axis=-1, descending=False)\nquantile(a, q, axis=None)\n\nto_numpy(a)\njit_to_numpy(a)  # Caches results for `B.jit`.\n```\n\n### Linear Algebra\n```\ntranspose(a, perm=None) (alias: t, T)\nmatmul(a, b, tr_a=False, tr_b=False) (alias: mm, dot)\neinsum(equation, *elements)\ntrace(a, axis1=0, axis2=1)\nkron(a, b)\nsvd(a, compute_uv=True)\neig(a, compute_eigvecs=True)\nsolve(a, b)\ninv(a)\npinv(a)\ndet(a) \nlogdet(a) \nexpm(a)\nlogm(a)\ncholesky(a) (alias: chol)\n\ncholesky_solve(a, b)  (alias: cholsolve)\ntriangular_solve(a, b, lower_a=True) (alias: trisolve)\ntoeplitz_solve(a, b, c) (alias: toepsolve)\ntoeplitz_solve(a, c)\n\nouter(a, b)\nreg(a, diag=None, clip=True)\n\npw_dists2(a, b)\npw_dists2(a)\npw_dists(a, b)\npw_dists(a)\n\new_dists2(a, b)\new_dists2(a)\new_dists(a, b)\new_dists(a)\n\npw_sums2(a, b)\npw_sums2(a)\npw_sums(a, b)\npw_sums(a)\n\new_sums2(a, b)\new_sums2(a)\new_sums(a, b)\new_sums(a)\n```\n\n### Random\n```\nset_random_seed(seed) \ncreate_random_state(dtype, seed=0)\nglobal_random_state(dtype)\nglobal_random_state(a)\nset_global_random_state(state)\n\nrand(state, dtype, *shape)\nrand(dtype, *shape)\nrand(*shape)\nrand(state, ref)\nrand(ref)\n\nrandn(state, dtype, *shape)\nrandn(dtype, *shape)\nrandn(*shape)\nrandn(state, ref)\nrandn(ref)\n\nchoice(a, n)\nchoice(a)\n\nrandint(state, dtype, *shape, lower=0, upper)\nrandint(dtype, *shape, lower=0, upper)\nrandint(*shape, lower=0, upper)\nrandint(state, ref, lower=0, upper)\nrandint(ref, lower=0, upper)\n\nrandperm(state, dtype, n)\nrandperm(dtype, n)\nrandperm(n)\n```\n\n### Shaping\n```\nshape(a, *dims)\nrank(a)\nlength(a) (alias: size)\nis_scalar(a)\nexpand_dims(a, axis=0, times=1)\nsqueeze(a, axis=None)\nuprank(a, rank=2)\ndownrank(a, rank=2, preserve=False)\nbroadcast_to(a, *shape)\n\ndiag(a)\ndiag_extract(a)\ndiag_construct(a)\nflatten(a)\nvec_to_tril(a, offset=0)\ntril_to_vec(a, offset=0)\nstack(*elements, axis=0)\nunstack(a, axis=0, squeeze=True)\nreshape(a, *shape)\nconcat(*elements, axis=0)\nconcat2d(*rows)\ntile(a, *repeats)\ntake(a, indices_or_mask, axis=0)\nsubmatrix(a, indices_or_mask)\n```\n\n## Devices\nYou can get the device of a tensor with `B.device(a)`,\nand you can execute a computation on a device by entering `B.on_device(device)`\nas a  context:\n\n```python\nwith B.on_device(\"gpu:0\"):\n    a = B.randn(tf.float32, 2, 2)\n    b = B.randn(tf.float32, 2, 2)\n    c = a @ b\n```\n\nWithin such a context, a tensor that is not on the active device can be moved to the\nactive device with `B.to_active_device(a)`.\n\nYou can also globally set the active device with `B.set_global_device(\"gpu:0\")`.\n\n## Lazy Shapes\nIf a function is evaluated abstractly, then elements of the shape of a tensor, e.g.\n`B.shape(a)[0]`, may also be tensors, which can break dispatch.\nBy entering `B.lazy_shapes()`, shapes and elements of shapes will be wrapped in a custom \ntype to fix this issue.\n\n```python\nwith B.lazy_shapes():\n    a = B.eye(2)\n    print(type(B.shape(a)))\n    # <class 'lab.shape.Shape'>\n    print(type(B.shape(a)[0]))\n    # <class 'lab.shape.Dimension'>\n```\n\n## Random Numbers\nIf you call a random number generator without providing a random state, e.g.\n`B.randn(np.float32, 2)`, the global random state from the corresponding\nbackend is used.\nFor JAX, since there is no global random state, LAB provides a JAX global\nrandom state accessible through `B.jax_global_random_state` once `lab.jax`\nis loaded.\n\nIf you do not want to use a global random state but rather explicitly maintain\none, you can create a random state with `B.create_random_state` and then\npass this as the first argument to the random number generators.\nThe random number generators will then return a tuple containing the updated\nrandom state and the random result.\n\n```python\n# Create random state.\nstate = B.create_random_state(tf.float32, seed=0)\n\n# Generate two random arrays.\nstate, x = B.randn(state, tf.float32, 2)\nstate, y = B.randn(state, tf.float32, 2)\n```\n\n\n## Control Flow Cache\nComing soon!\n",
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