Fix broken docs link

Author: SurbhiJainUSC

docs/guides/data_input_pipeline/data_input_grain.md

@@ -15,7 +15,7 @@
 
 ## Why is determinism important for a data input pipeline?
 
-Determinism in a data input pipeline means that the same input data always results in the same sequence of batches at each step. This is typically achieved by setting a fixed [shuffle seed](https://google-grain.readthedocs.io/en/stable/grain.dataset.html#grain.MapDataset.shuffle) during pipeline initialization. In an ideal scenario, where training runs uninterrupted, this determinism is straightforward (deterministic without preemption). However, real-world distributed training environments often face preemptions due to maintenance, hardware failures, or resource constraints. When a preempted training run resumes, the data input pipeline is re-initialized. If the same shuffle seed is used, the pipeline restarts from the beginning, potentially re-training the model on initial data. Conversely, a new seed produces a different batch sequence, making it difficult to track which data has been seen and how often each example is used for training. This lack of control can impact model performance and reproducibility.
+Determinism in a data input pipeline means that the same input data always results in the same sequence of batches at each step. This is typically achieved by setting a fixed [shuffle seed](https://google-grain.readthedocs.io/en/stable/grain.dataset.html#grain.MapDataset) during pipeline initialization. In an ideal scenario, where training runs uninterrupted, this determinism is straightforward (deterministic without preemption). However, real-world distributed training environments often face preemptions due to maintenance, hardware failures, or resource constraints. When a preempted training run resumes, the data input pipeline is re-initialized. If the same shuffle seed is used, the pipeline restarts from the beginning, potentially re-training the model on initial data. Conversely, a new seed produces a different batch sequence, making it difficult to track which data has been seen and how often each example is used for training. This lack of control can impact model performance and reproducibility.
 
 ## How Grain achieves determinism
 

docs/guides/monitoring_and_debugging/features_and_diagnostics.md

@@ -80,7 +80,7 @@ Ahead-of-time compilation is also supported for GPUs with some differences from
 
 1. GPU does not support compilation across hardware: A GPU host is still required to run AoT compilation, but a single GPU host can compile a program for a larger cluster of the same hardware.
 
-2. For [A3 Cloud GPUs](https://cloud.google.com/compute/docs/gpus#h100-gpus), the maximum "slice" size is a single host, and the `compile_topology_num_slices` parameter represents the number of A3 machines to precompile for.
+2. For [A3 Cloud GPUs](https://docs.cloud.google.com/compute/docs/gpus#h100-gpus), the maximum "slice" size is a single host, and the `compile_topology_num_slices` parameter represents the number of A3 machines to precompile for.
 
 #### Example
 

docs/guides/monitoring_and_debugging/use_vertex_ai_tensorboard.md

@@ -28,7 +28,7 @@ You can use a single Vertex AI Tensorboard instance to track and compare metrics
 
 ## Prerequisites
 
-- Enable [Vertex AI API](https://docs.cloud.google.com/vertex-ai/docs/start/cloud-environment#set_up_a_project) in your Google Cloud console.
+- Enable [Vertex AI API](https://cloud.google.com/vertex-ai/docs/start/cloud-environment#set_up_a_project) in your Google Cloud console.
 - Assign [Vertex AI User IAM role](https://cloud.google.com/vertex-ai/docs/general/access-control#aiplatform.user) to the service account used by the TPU VMs. This is required to create and access the Vertex AI Tensorboard in Google Cloud console. If you are using XPK for MaxText, the necessary Vertex AI User IAM role will be automatically assigned to your node pools by XPK – no need to assign it manually.
 
 ## Upload logs to Vertex AI Tensorboard