An example does not expound the rule. Here you could get the expected result* using:

XSLT 1.0

<xsl:stylesheet version="1.0"  xmlns:xsl="http://www.w3.org/1999/XSL/Transform"> <xsl:output method="xml" version="1.0" encoding="UTF-8" indent="yes"/> <xsl:strip-space elements="*"/>  <!-- identity transform --> <xsl:template match="@*|node()">     <xsl:copy>         <xsl:apply-templates select="@*|node()"/>     </xsl:copy> </xsl:template>  <xsl:template match="span[@class='tt']/text()[1]">     <xsl:value-of select="substring-after(., '. ')"/> </xsl:template>  </xsl:stylesheet> 

Whether that would fit all your cases is not clear.

(*) Provided you fix your input to be well-formed XML: <strong> does not match </Strong>.

For retry policies, you can also use an external library like Polly.

You could use a FuncFormatter:

from matplotlib.ticker import FuncFormatter  ax.yaxis.set_major_formatter(FuncFormatter(lambda x,_: '0.00' if x == 0 else f'{x:.4f}')) 

(Project name:\s+[A-Z]:(?:\\w+)+.[a-zA-Z]+\s+J[0-9]{7})(?=:)

This will work for you.

Adding (?:\\w+)+.[a-zA-Z]+ will be more restrictive instead of .*

from flask import Flask from flask_restplus import Api, Resource from werkzeug.utils import cached_property  import tensorflow as tf import tensorflow_hub as hub module_url = "https://tfhub.dev/google/universal-sentence-encoder-qa/3" embedding_fn = hub.load(module_url)   app = Flask(__name__)  @app.route('/embedding', methods=['POST']) def entry_point(args):     if args.get("text"):         text_term = args.get("text")         if isinstance(text_term, str):             text_term = [text_term]         vectors = convert_text_to_vector(text_term)     return vectors    @tf.function def convert_text_to_vector(text):     try:         vector_array = embedding_fn.signatures['question_encoder'](tf.constant(text))         return vector_array['outputs']     except Exception as err:         raise Exception(str(err))   if __name__ == '__main__':     app.run(host='0.0.0.0', port=5000, debug=True)  """  ----- Requirements.txt ---- flask-restplus==0.13.0 Flask==1.1.1 Werkzeug==0.15.5 tensorboard==2.2.2 tensorboard-plugin-wit==1.6.0.post3 tensorflow==2.2.0 tensorflow-estimator==2.2.0 tensorflow-hub==0.8.0 tensorflow-text==2.2.1 """ 

you need to define class_mode='binary' also in image_gen_train.flow_from_directory

here the running code: https://colab.research.google.com/drive/1tisXyQFLHet0vrLdmyAxKNOwHMmCayXu?usp=sharing

You can define what Objects, functions and classes get imported with import * from your module with __all__

in the beginning of your module add:

__all__ = []

Everything you put in that list can be imported with from yourmodule import *. Everything else that is not named can still be accessed with yourmodule.objectname.

Example: Let’s assume you have 2 functions in your module. E.g.:

def foo():     print("Foo")  def bar():     print("Bar") 

Importing * from your module will import foo and bar.

If you add __all__ = ['foo'] then import * will only import foo. And if you add __all__ = [] then import * should not import anything.

Edit: if you leave the list empty, nothing will be imported, however if you want an error to be raised so that users understand that you don’t want to allow import *, then add one entry that will fail, e.g.: __all__=['NO_WILDCARD_IMPORT_ALLOWED'].

This should work for you:

my_list = [] for i in range(len(files)):     my_list.append([files[i],firstlines[i]]) 

if you want to use the key-value structure, then you have to use Python’s dictionaries. It could look like this:

my_dict = {} for i in range(len(files)):     my_list[files[i]] = firstlines[i] 

You have to use "setattr"

class A:     def __init__(self):         self.var_a = 'var_a'  class B:     def __init__(self):         self.var_b = A()         self.var_c = A() a = B() setattr(a.var_b, 'var_d', 1) 

leads to

print(a.var_b.var_d) >> 1 

Thanks to j_4321 suggestion and the code from the response in the linked question, I came up with the following solution. It is not perfect, as I still need to manually adjust the values for different sized plots. It also needs automatic aspect, which skews the heatmap somewhat:

import numpy as np import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec  def draw_brace(ax, span, text, axis):     """Draws an annotated brace on the axes."""     # axis could be reversed     xx = ax.get_xlim()     xmin = np.min(xx)     xmax = np.max(xx)     yy = ax.get_ylim()     ymin = np.min(yy)     ymax = np.max(yy)     xspan = xmax - xmin     yspan = ymax - ymin          if axis=="y":         tspan = yspan         ospan = xspan         omin  = xmin     else:         ospan = yspan         omin  = ymin         tspan = xspan          amin, amax = span     span = amax - amin          resolution = int(span/tspan*100)*2+1 # guaranteed uneven     beta = 300./tspan # the higher this is, the smaller the radius          x = np.linspace(amin, amax, resolution)     x_half = x[:resolution//2+1]     y_half_brace = (1/(1.+np.exp(-beta*(x_half-x_half[0])))                     + 1/(1.+np.exp(-beta*(x_half-x_half[-1]))))     y = np.concatenate((y_half_brace, y_half_brace[-2::-1]))     y = omin + (.05*y - .01)*ospan # adjust vertical position      #ax.autoscale(False)     if axis == "y":         ax.plot(-y +1 , x, color='black', lw=1)         ax.text(0.8+ymin+.07*yspan, (amax+amin)/2., text, ha='center', va='center')     else:         ax.plot(x, y, color='black', lw=1)         ax.text((amax+amin)/2.,ymin+.07*yspan, text, ha='center', va='center')       arr = np.array([[3,4],[2,3.5],[10,11],[9,10]]) fig = plt.figure()  gs = fig.add_gridspec(nrows=1, ncols=2, wspace=0,width_ratios=[1,4])  ax2 = fig.add_subplot(gs[:, 1])  ax2.imshow(arr) ax2.set_title("example plot")  ax2.set_yticklabels([]) ax2.set_yticks([]) ax2.set_xticklabels([]) ax2.set_aspect('auto')  ax1 = fig.add_subplot(gs[:, 0], sharey=ax2) ax1.set_xticks([]) ax1.set_xticklabels([]) ax1.set_aspect('auto') ax1.set_xlim([0,1]) ax1.axis('off') draw_brace(ax1, (0, 1), 'group1',"y") draw_brace(ax1, (2, 3), 'group2',"y")  fig.subplots_adjust(wspace=0, hspace=0) 

This creates the following plot: