From 3d2c8dccf356da842f39bb114848c21a357e81f3 Mon Sep 17 00:00:00 2001
From: Rakshit Kumar Singh <rakshitsingh421@gmail.com>
Date: Fri, 12 Jan 2024 16:32:02 +0000
Subject: [PATCH] minor fixes

---
 new-website/utils/tutorials/build_pdf_book.py |  2 +-
 new-website/utils/tutorials/contents.md       | 88 +++++++++----------
 2 files changed, 45 insertions(+), 45 deletions(-)

diff --git a/new-website/utils/tutorials/build_pdf_book.py b/new-website/utils/tutorials/build_pdf_book.py
index 376d6e2e..1d45a176 100644
--- a/new-website/utils/tutorials/build_pdf_book.py
+++ b/new-website/utils/tutorials/build_pdf_book.py
@@ -73,7 +73,7 @@ def merge_pdf_pages(a: List[str]):
     command = "pdfunite "
     for i in a:
         command = command + i + ' '
-    os.system(command, "merged.pdf")
+    os.system(command + "merged.pdf")
 
 def compile_information_pages():
     """Converts the Acknowledgent page and content page from
diff --git a/new-website/utils/tutorials/contents.md b/new-website/utils/tutorials/contents.md
index ee0f902b..0f919105 100644
--- a/new-website/utils/tutorials/contents.md
+++ b/new-website/utils/tutorials/contents.md
@@ -1,45 +1,45 @@
 # Conents
-About_nODE_Using_Torchdiffeq_in_Deepchem
-Advanced_Model_Training
-Advanced_model_training_using_hyperopt
-An_Introduction_To_MoleculeNet
-Atomic_Contributions_for_Molecules
-Conditional_Generative_Adversarial_Networks
-Creating_Models_with_TensorFlow_and_PyTorch
-Creating_a_high_fidelity_model_from_experimental_data
-DeepQMC_tutorial
-Deep_probabilistic_analysis_of_single-cell_omics_data
-Exploring_Quantum_Chemistry_with_GDB1k
-Generating_molecules_with_MolGAN
-Going_Deeper_on_Molecular_Featurizations
-Hierarchial_Moelcular_Generation_with_HierVAE
-Interactive_Model_Evaluation_with_Trident_Chemwidgets
-Introducing_JaxModel_and_PINNModel
-Introduction_To_Material_Science
-Introduction_to_Bioinformatics
-Introduction_to_Equivariance
-Introduction_to_GROVER
-Introduction_to_Gaussian_Processes
-Introduction_to_Graph_Convolutions
-Introduction_to_Model_Interpretability
-Introduction_to_Molecular_Attention_Transformer
-Large_Scale_Chemical_Screens
-Learning_Unsupervised_Embeddings_for_Molecules
-Modeling_Protein_Ligand_Interactions
-Modeling_Protein_Ligand_Interactions_With_Atomic_Convolutions
-Molecular_Fingerprints
-Multisequence_Alignments
-Physics_Informed_Neural_Networks
-Protein_Deep_Learning
-Putting_Multitask_Learning_to_Work
-PytorchLightning_Integration
-Scanpy
-The_Basic_Tools_of_the_Deep_Life_Sciences
-Training_a_Generative_Adversarial_Network_on_MNIST
-Training_a_Normalizing_Flow_on_QM9
-Training_an_Exchange_Correlation_Functional_using_Deepchem
-Transfer_Learning_With_ChemBERTa_Transformers
-Uncertainty_In_Deep_Learning
-Using_Reinforcement_Learning_to_Play_Pong
-Working_With_Datasets
-Working_With_Splitters
+- About_nODE_Using_Torchdiffeq_in_Deepchem
+- Advanced_Model_Training
+- Advanced_model_training_using_hyperopt
+- An_Introduction_To_MoleculeNet
+- Atomic_Contributions_for_Molecules
+- Conditional_Generative_Adversarial_Networks
+- Creating_Models_with_TensorFlow_and_PyTorch
+- Creating_a_high_fidelity_model_from_experimental_data
+- DeepQMC_tutorial
+- Deep_probabilistic_analysis_of_single-cell_omics_data
+- Exploring_Quantum_Chemistry_with_GDB1k
+- Generating_molecules_with_MolGAN
+- Going_Deeper_on_Molecular_Featurizations
+- Hierarchial_Moelcular_Generation_with_HierVAE
+- Interactive_Model_Evaluation_with_Trident_Chemwidgets
+- Introducing_JaxModel_and_PINNModel
+- Introduction_To_Material_Science
+- Introduction_to_Bioinformatics
+- Introduction_to_Equivariance
+- Introduction_to_GROVER
+- Introduction_to_Gaussian_Processes
+- Introduction_to_Graph_Convolutions
+- Introduction_to_Model_Interpretability
+- Introduction_to_Molecular_Attention_Transformer
+- Large_Scale_Chemical_Screens
+- Learning_Unsupervised_Embeddings_for_Molecules
+- Modeling_Protein_Ligand_Interactions
+- Modeling_Protein_Ligand_Interactions_With_Atomic_Convolutions
+- Molecular_Fingerprints
+- Multisequence_Alignments
+- Physics_Informed_Neural_Networks
+- Protein_Deep_Learning
+- Putting_Multitask_Learning_to_Work
+- PytorchLightning_Integration
+- Scanpy
+- The_Basic_Tools_of_the_Deep_Life_Sciences
+- Training_a_Generative_Adversarial_Network_on_MNIST
+- Training_a_Normalizing_Flow_on_QM9
+- Training_an_Exchange_Correlation_Functional_using_Deepchem
+- Transfer_Learning_With_ChemBERTa_Transformers
+- Uncertainty_In_Deep_Learning
+- Using_Reinforcement_Learning_to_Play_Pong
+- Working_With_Datasets
+- Working_With_Splitters