message: Fix crop on images in datasets_performance guide.
diff: @@ -92,11 +92,11 @@ transform the data.
  Without pipelining, the CPU and the GPU/TPU sit idle much of the time:
  -![without pipelining](https://www.tensorflow.org/images/datasets_without_pipelining.png)
  +![without pipelining](/images/datasets_without_pipelining.png)
  With pipelining, idle time diminishes significantly:
  -![with pipelining](https://www.tensorflow.org/images/datasets_with_pipelining.png)
  +![with pipelining](/images/datasets_with_pipelining.png)
  The `tf.data` API provides a software pipelining mechanism through the
  @{tf.data.Dataset.prefetch} transformation, which can be used to decouple the
  @@ -139,7 +139,7 @@ multiple CPU cores. To make this possible, the `map` transformation provides the
  the following diagram illustrates the effect of setting `num_parallel_calls=2`
  to the `map` transformation:
  -![parallel map](https://www.tensorflow.org/images/datasets_parallel_map.png)
  +![parallel map](/images/datasets_parallel_map.png)
  Choosing the best value for the `num_parallel_calls` argument depends on your
  hardware, characteristics of your training data (such as its size and shape),
  @@ -213,7 +213,7 @@ number of datasets to overlap can be specified by the `cycle_length` argument.
  The following diagram illustrates the effect of supplying `cycle_length=2` to
  the `parallel_interleave` transformation:
  -![parallel io](https://www.tensorflow.org/images/datasets_parallel_io.png)
  +![parallel io](/images/datasets_parallel_io.png)
  To apply this change to our running example, change:
  