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"""Captures and returns an argument passed to a lambda expression."""

class Capture:
    """Captures and returns an argument passed to a lambda expression.

    :param fn: The lambda expression.
    :type fn: function

    :Example:

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import numpy as np


class EWMA:
    """An exponentially weighted moving average with initialization bias correction."""
    def __init__(self, shape=(), dtype=np.float64, beta=0.9, correct_bias=True):
        self.beta = beta
        self.beta_accum = 1 if correct_bias else 0
        self.value = np.zeros(shape, dtype)

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import pyparsing as pp
from pyparsing import pyparsing_common as ppc

from repl import repl


class Node:
    def __init__(self, name, *args):
        self.name = name
        self.items = args

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import pyparsing as pp
from pyparsing import pyparsing_common as ppc

from repl import repl


variables = {}

var = ppc.identifier.copy()
def do_var(t):

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"""Converts between the RGB and CIECAM02 color spaces."""

from collections import namedtuple
from functools import partial

import colour
from colour.utilities import tsplit, tstack
import numpy as np
from scipy.optimize import fmin_l_bfgs_b

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"""Converts colors from HSL to RGB and back."""

import numpy as np
from scipy import optimize


def tstack(a):
    """Stacks arrays in sequence along the last axis (tail)."""
    a = np.asarray(a)
    return np.concatenate([x[..., np.newaxis] for x in a], axis=-1)

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import argparse

from functools import partial, reduce
import math
from pathlib import Path
import random

from keras.layers import *
from keras.models import Model
from keras.utils import io_utils

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name: "VGG_ILSVRC_16_layers_conv"
force_backward: true
layer {
  name: "data"
  type: "Input"
  top: "data"
  input_param { shape: { dim: 1 dim: 3 dim: 224 dim: 224 } }
}
layer {
  bottom: "data"

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import numpy as np
from scipy.linalg import blas
from scipy.ndimage import zoom

# Machine epsilon for float32
EPS = np.finfo(np.float32).eps


# pylint: disable=no-member
def dot(x, y):

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--[[ An experimental quasi-Newton optimizer.

Incorporates Hessian damping, momentum, and per-feature learning rate scaling.
Also implements optional polynomial-decay averaging (similar to ASGD).

ARGS:
- 'opfunc' : a function that takes a single input (X), the point
             of a evaluation, and returns f(X) and df/dX
- 'x'      : the initial point
- 'config` : a table with configuration parameters for the optimizer