TensorFlow 2 User Guide: Updating links to Graphcore examples repo

Summary:
The structure of the Graphcore Examples repo on GitHub has changed. This diff updates the following:

 * the links to refer to the updated location for the referenced examples
 * updates the example links to refer to TensorFlow 2.

TF2.6 Only

Test Plan: docs-only!

Reviewers: #tensorflow, #framework_ip_review_-_any_oss_or_third-party_code_use_has_been_approved, alfiee

Reviewed By: #tensorflow, #framework_ip_review_-_any_oss_or_third-party_code_use_has_been_approved, alfiee

Subscribers: alfiee

Maniphest Tasks: T65489

Differential Revision: https://phabricator.sourcevertex.net/D70714

Author: jayniep-gc

tensorflow/compiler/plugin/poplar/docs/poplar_ops.rst

@@ -7,28 +7,28 @@ available in PopLibs. See the :ref:`api-section` for more details.
 Image operations
 ~~~~~~~~~~~~~~~~
 
-Our architecture is well-suited to efficiently handle convolutions over four-channel tensors, however it is common 
-for images to be represented with three channels. 
-In order to obtain better IPU performance, both from a latency and memory standpoint, we advise that when 
-dealing with three-channel inputs, you pad the fourth channel dimension. 
+Our architecture is well-suited to efficiently handle convolutions over four-channel tensors, however it is common
+for images to be represented with three channels.
+In order to obtain better IPU performance, both from a latency and memory standpoint, we advise that when
+dealing with three-channel inputs, you pad the fourth channel dimension.
 
-See :py:func:`tensorflow.python.ipu.image_ops.normalise_image` 
-for the op that can perform this padding, in addition to normalising and casting if needed. Note that this padding will be 
-performed on-device, after the data has been transferred to the IPU. 
+See :py:func:`tensorflow.python.ipu.image_ops.normalise_image`
+for the op that can perform this padding, in addition to normalising and casting if needed. Note that this padding will be
+performed on-device, after the data has been transferred to the IPU.
 
 An example of its use can be found in the ``fused_normalise_image()`` function in the `CNN training application
-example <https://github.com/graphcore/examples/blob/master/applications/tensorflow/cnns/training/Datasets/imagenet_preprocessing.py>`_ 
-in Graphcore's examples repository on GitHub. 
+example <https://github.com/graphcore/examples/blob/master/vision/cnns/tensorflow1/training/Datasets/imagenet_preprocessing.py>`_
+in Graphcore's examples repository on GitHub.
 
 Matmul serialisation
 ~~~~~~~~~~~~~~~~~~~~
 
-You have the option to serialise matrix multiplications along a particular dimension, in order to reduce 
+You have the option to serialise matrix multiplications along a particular dimension, in order to reduce
 the code size of the multiplication and the temporary memory requirements of the matmul, at the expense of extra computation.
 
-See :py:func:`tensorflow.python.ipu.math_ops.serialized_matmul` for details of the op. 
+See :py:func:`tensorflow.python.ipu.math_ops.serialized_matmul` for details of the op.
 
-An example of its use can be found in the ``mlm_head()`` function in the `BERT application example <https://github.com/graphcore/examples/blob/master/applications/tensorflow/bert/modeling.py>`_ 
+An example of its use can be found in the ``mlm_head()`` function in the `BERT application example <https://github.com/graphcore/examples/blob/master/nlp/bert/tensorflow1/modeling.py>`_
 in Graphcore's examples repository on GitHub.
 
 Dropout