sub-object in the response. In this case, we want to create a links sub-object to contain urls of related objects. Note that we passed fields.Nested another dict which is built in such a way that it would Looks up the representation transformer for the requested media type, invoking the transformer to create a response object. This defaults to default_mediatype if no transformer is found for the requested mediatype the transformer represents. Three arguments are passed to the transformer: • The data to be represented in the response body • The http status code • A dictionary of headers The transformer should

sub-object in the response. In this case, we want to create a links sub-object to contain urls of related objects. Note that we passed fields.Nested another dict which is built in such a way that it would Looks up the representation transformer for the requested media type, invoking the transformer to create a response object. This defaults to default_mediatype if no transformer is found for the requested mediatype the transformer represents. Three arguments are passed to the transformer: The data to be represented in the response body The http status code A dictionary of headers The transformer should convert

sub-object in the response. In this case, we want to create a links sub-object to contain urls of related objects. Note that we passed fields.Nested another dict which is built in such a way that it would Looks up the representation transformer for the requested media type, invoking the transformer to create a response object. This defaults to default_mediatype if no transformer is found for the requested mediatype the transformer represents. Three arguments are passed to the transformer: • The data to be represented in the response body • The http status code • A dictionary of headers The transformer should

sub-object in the response. In this case, we want to create a links sub-object to contain urls of related objects. Note that we passed fields.Nested another dict which is built in such a way that it would Looks up the representation transformer for the requested media type, invoking the transformer to create a response object. This defaults to default_mediatype if no transformer is found for the requested mediatype the transformer represents. Three arguments are passed to the transformer: • The data to be represented in the response body • The http status code • A dictionary of headers The transformer should

sub-object in the response. In this case, we want to create a links sub-object to contain urls of related objects. Note that we passed fields.Nested another dict which is built in such a way that it would Looks up the representation transformer for the requested media type, invoking the transformer to create a response object. This defaults to default_mediatype if no transformer is found for the requested mediatype the transformer represents. Three arguments are passed to the transformer: The data to be represented in the response body The http status code A dictionary of headers The transformer should convert

sub-object in the response. In this case, we want to create a links sub-object to contain urls of related objects. Note that we passed fields.Nested another dict which is built in such a way that it would Looks up the representation transformer for the requested media type, invoking the transformer to create a response object. This defaults to default_mediatype if no transformer is found for the requested mediatype the transformer represents. Three arguments are passed to the transformer: The data to be represented in the response body The http status code A dictionary of headers The transformer should convert

the %run magic command. Typically, you will work on a computational problem in pieces, organizing related ideas into cells and moving forward once previous parts work correctly. This is much more convenient documented in the configuration file and the user documentation. • Running a Notebook server • Related: Configuring a language kernel to run in the Notebook server enables your server to run other languages cell.cell_type == 'code': # transform the input to executable Python code = self.shell.input_transformer_manager.transform_cell(cell.source) # run the code in themodule exec(code, mod.__dict__) finally:

the %run magic command. Typically, you will work on a computational problem in pieces, organizing related ideas into cells and moving forward once previous parts work correctly. This is much more convenient documented in the configuration file and the user documentation. • Running a Notebook server • Related: Configuring a language kernel to run in the Notebook server enables your server to run other languages cell.cell_type == 'code': # transform the input to executable Python code = self.shell.input_transformer_manager.transform_cell(cell.source) # run the code in themodule exec(code, mod.__dict__) finally:

the %run magic command. Typically, you will work on a computational problem in pieces, organizing related ideas into cells and moving forward once previous parts work correctly. This is much more convenient documented in the configuration file and the user documentation. • Running a Notebook server • Related: Configuring a language kernel to run in the Notebook server enables your server to run other languages cell.cell_type == 'code': # transform the input to executable Python code = self.shell.input_transformer_manager.transform_cell(cell.source) # run the code in themodule exec(code, mod.__dict__) finally:

the %run magic command. Typically, you will work on a computational problem in pieces, organizing related ideas into cells and moving forward once previous parts work correctly. This is much more convenient are documented in the configuration file and the user documentation. Running a Notebook server Related: Configuring a language kernel [https://jupyter.readthedocs.io/en/latest/install.html#installing-kernels] # transform the input to executable Python code = self.shell.input_transformer_manager.transform_cell(cell.sou rce) # run the code in themodule