From ffe02f93a5e7e6e56573e7135acb380b4e79117f Mon Sep 17 00:00:00 2001
From: qiskit-crowdin-bot
 <54556828+qiskit-crowdin-bot@users.noreply.github.com>
Date: Wed, 29 Nov 2023 09:50:23 -0500
Subject: [PATCH] New translations 05_torch_connector.po (Marathi)

---
 .../tutorials/05_torch_connector.po           | 22 +++++++++----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/machine-learning/docs/locale/mr_IN/LC_MESSAGES/tutorials/05_torch_connector.po b/machine-learning/docs/locale/mr_IN/LC_MESSAGES/tutorials/05_torch_connector.po
index e5894c430de090..335ec1e792332d 100644
--- a/machine-learning/docs/locale/mr_IN/LC_MESSAGES/tutorials/05_torch_connector.po
+++ b/machine-learning/docs/locale/mr_IN/LC_MESSAGES/tutorials/05_torch_connector.po
@@ -2,8 +2,8 @@ msgid ""
 msgstr ""
 "Project-Id-Version: qiskit-docs\n"
 "Report-Msgid-Bugs-To: \n"
-"POT-Creation-Date: 2023-11-10 17:18+0000\n"
-"PO-Revision-Date: 2023-11-10 18:27\n"
+"POT-Creation-Date: 2023-11-29 12:46+0000\n"
+"PO-Revision-Date: 2023-11-29 14:50\n"
 "Last-Translator: \n"
 "Language: mr\n"
 "Language-Team: Marathi\n"
@@ -172,39 +172,39 @@ msgstr ""
 msgid "We take advantage of the ``torchvision`` `API <https://pytorch.org/vision/stable/datasets.html>`__ to directly load a subset of the `MNIST dataset <https://en.wikipedia.org/wiki/MNIST_database>`__ and define torch ``DataLoader``\\ s (`link <https://pytorch.org/docs/stable/data.html>`__) for train and test."
 msgstr ""
 
-#: ../../tutorials/05_torch_connector.ipynb:1373
+#: ../../tutorials/05_torch_connector.ipynb:1348
 msgid "If we perform a quick visualization we can see that the train dataset consists of images of handwritten 0s and 1s."
 msgstr ""
 
-#: ../../tutorials/05_torch_connector.ipynb:1447
+#: ../../tutorials/05_torch_connector.ipynb:1422
 msgid "Step 2: Defining the QNN and Hybrid Model"
 msgstr ""
 
-#: ../../tutorials/05_torch_connector.ipynb:1458
+#: ../../tutorials/05_torch_connector.ipynb:1433
 msgid "This second step shows the power of the ``TorchConnector``. After defining our quantum neural network layer (in this case, a ``EstimatorQNN``), we can embed it into a layer in our torch ``Module`` by initializing a torch connector as ``TorchConnector(qnn)``."
 msgstr ""
 
-#: ../../tutorials/05_torch_connector.ipynb:1460
+#: ../../tutorials/05_torch_connector.ipynb:1435
 msgid "**⚠️ Attention:** In order to have an adequate gradient backpropagation in hybrid models, we MUST set the initial parameter ``input_gradients`` to TRUE during the qnn initialization."
 msgstr ""
 
-#: ../../tutorials/05_torch_connector.ipynb:1539
+#: ../../tutorials/05_torch_connector.ipynb:1514
 msgid "Step 3: Training"
 msgstr ""
 
-#: ../../tutorials/05_torch_connector.ipynb:1653
+#: ../../tutorials/05_torch_connector.ipynb:1628
 msgid "Now we'll save the trained model, just to show how a hybrid model can be saved and re-used later for inference. To save and load hybrid models, when using the TorchConnector, follow the PyTorch recommendations of saving and loading the models."
 msgstr ""
 
-#: ../../tutorials/05_torch_connector.ipynb:1675
+#: ../../tutorials/05_torch_connector.ipynb:1650
 msgid "Step 4: Evaluation"
 msgstr ""
 
-#: ../../tutorials/05_torch_connector.ipynb:1686
+#: ../../tutorials/05_torch_connector.ipynb:1661
 msgid "We start from recreating the model and loading the state from the previously saved file. You create a QNN layer using another simulator or a real hardware. So, you can train a model on real hardware available on the cloud and then for inference use a simulator or vice verse. For a sake of simplicity we create a new quantum neural network in the same way as above."
 msgstr ""
 
-#: ../../tutorials/05_torch_connector.ipynb:1834
+#: ../../tutorials/05_torch_connector.ipynb:1809
 msgid "🎉🎉🎉🎉 **You are now able to experiment with your own hybrid datasets and architectures using Qiskit Machine Learning.** **Good Luck!**"
 msgstr ""