chore(all): replace un-inclusive language (#675)

diff --git a/google-api-go-generator/testdata/json-body.json b/google-api-go-generator/testdata/json-body.json
index a78e9b6..27642c7 100644
--- a/google-api-go-generator/testdata/json-body.json
+++ b/google-api-go-generator/testdata/json-body.json
@@ -451,7 +451,7 @@
                   "type": "string"
                 },
                 "parent": {
-                  "description": "Required. The name of the project for which available locations are to be\nlisted (since some locations might be whitelisted for specific projects).",
+                  "description": "Required. The name of the project for which available locations are to be\nlisted (since some locations might be restricted to specific projects).",
                   "location": "path",
                   "pattern": "^projects/[^/]+$",
                   "required": true,
@@ -1798,8 +1798,8 @@
           "description": "Optional. A Google Cloud Storage path in which to store training outputs\nand other data needed for training. This path is passed to your TensorFlow\nprogram as the '--job-dir' command-line argument. The benefit of specifying\nthis field is that Cloud ML validates the path for use in training.",
           "type": "string"
         },
-        "masterType": {
-          "description": "Optional. Specifies the type of virtual machine to use for your training\njob's master worker.\n\nThe following types are supported:\n\n\u003cdl\u003e\n  \u003cdt\u003estandard\u003c/dt\u003e\n  \u003cdd\u003e\n  A basic machine configuration suitable for training simple models with\n  small to moderate datasets.\n  \u003c/dd\u003e\n  \u003cdt\u003elarge_model\u003c/dt\u003e\n  \u003cdd\u003e\n  A machine with a lot of memory, specially suited for parameter servers\n  when your model is large (having many hidden layers or layers with very\n  large numbers of nodes).\n  \u003c/dd\u003e\n  \u003cdt\u003ecomplex_model_s\u003c/dt\u003e\n  \u003cdd\u003e\n  A machine suitable for the master and workers of the cluster when your\n  model requires more computation than the standard machine can handle\n  satisfactorily.\n  \u003c/dd\u003e\n  \u003cdt\u003ecomplex_model_m\u003c/dt\u003e\n  \u003cdd\u003e\n  A machine with roughly twice the number of cores and roughly double the\n  memory of \u003ci\u003ecomplex_model_s\u003c/i\u003e.\n  \u003c/dd\u003e\n  \u003cdt\u003ecomplex_model_l\u003c/dt\u003e\n  \u003cdd\u003e\n  A machine with roughly twice the number of cores and roughly double the\n  memory of \u003ci\u003ecomplex_model_m\u003c/i\u003e.\n  \u003c/dd\u003e\n  \u003cdt\u003estandard_gpu\u003c/dt\u003e\n  \u003cdd\u003e\n  A machine equivalent to \u003ci\u003estandard\u003c/i\u003e that\n  also includes a single NVIDIA Tesla K80 GPU. See more about\n  \u003ca href=\"/ml-engine/docs/tensorflow/using-gpus\"\u003eusing GPUs to\n  train your model\u003c/a\u003e.\n  \u003c/dd\u003e\n  \u003cdt\u003ecomplex_model_m_gpu\u003c/dt\u003e\n  \u003cdd\u003e\n  A machine equivalent to \u003ci\u003ecomplex_model_m\u003c/i\u003e that also includes\n  four NVIDIA Tesla K80 GPUs.\n  \u003c/dd\u003e\n  \u003cdt\u003ecomplex_model_l_gpu\u003c/dt\u003e\n  \u003cdd\u003e\n  A machine equivalent to \u003ci\u003ecomplex_model_l\u003c/i\u003e that also includes\n  eight NVIDIA Tesla K80 GPUs.\n  \u003c/dd\u003e\n  \u003cdt\u003estandard_p100\u003c/dt\u003e\n  \u003cdd\u003e\n  A machine equivalent to \u003ci\u003estandard\u003c/i\u003e that\n  also includes a single NVIDIA Tesla P100 GPU.\n  \u003c/dd\u003e\n  \u003cdt\u003ecomplex_model_m_p100\u003c/dt\u003e\n  \u003cdd\u003e\n  A machine equivalent to \u003ci\u003ecomplex_model_m\u003c/i\u003e that also includes\n  four NVIDIA Tesla P100 GPUs.\n  \u003c/dd\u003e\n  \u003cdt\u003estandard_v100\u003c/dt\u003e\n  \u003cdd\u003e\n  A machine equivalent to \u003ci\u003estandard\u003c/i\u003e that\n  also includes a single NVIDIA Tesla V100 GPU. The availability of these\n  GPUs is in the \u003ci\u003eBeta\u003c/i\u003e launch stage.\n  \u003c/dd\u003e\n  \u003cdt\u003elarge_model_v100\u003c/dt\u003e\n  \u003cdd\u003e\n  A machine equivalent to \u003ci\u003elarge_model\u003c/i\u003e that\n  also includes a single NVIDIA Tesla V100 GPU. The availability of these\n  GPUs is in the \u003ci\u003eBeta\u003c/i\u003e launch stage.\n  \u003c/dd\u003e\n  \u003cdt\u003ecomplex_model_m_v100\u003c/dt\u003e\n  \u003cdd\u003e\n  A machine equivalent to \u003ci\u003ecomplex_model_m\u003c/i\u003e that\n  also includes four NVIDIA Tesla V100 GPUs. The availability of these\n  GPUs is in the \u003ci\u003eBeta\u003c/i\u003e launch stage.\n  \u003c/dd\u003e\n  \u003cdt\u003ecomplex_model_l_v100\u003c/dt\u003e\n  \u003cdd\u003e\n  A machine equivalent to \u003ci\u003ecomplex_model_l\u003c/i\u003e that\n  also includes eight NVIDIA Tesla V100 GPUs. The availability of these\n  GPUs is in the \u003ci\u003eBeta\u003c/i\u003e launch stage.\n  \u003c/dd\u003e\n  \u003cdt\u003ecloud_tpu\u003c/dt\u003e\n  \u003cdd\u003e\n  A TPU VM including one Cloud TPU. See more about\n  \u003ca href=\"/ml-engine/docs/tensorflow/using-tpus\"\u003eusing TPUs to train\n  your model\u003c/a\u003e.\n  \u003c/dd\u003e\n\u003c/dl\u003e\n\nYou must set this value when `scaleTier` is set to `CUSTOM`.",
+        "mainType": {
+          "description": "Optional. Specifies the type of virtual machine to use for your training\njob's main worker.\n\nThe following types are supported:\n\n\u003cdl\u003e\n  \u003cdt\u003estandard\u003c/dt\u003e\n  \u003cdd\u003e\n  A basic machine configuration suitable for training simple models with\n  small to moderate datasets.\n  \u003c/dd\u003e\n  \u003cdt\u003elarge_model\u003c/dt\u003e\n  \u003cdd\u003e\n  A machine with a lot of memory, specially suited for parameter servers\n  when your model is large (having many hidden layers or layers with very\n  large numbers of nodes).\n  \u003c/dd\u003e\n  \u003cdt\u003ecomplex_model_s\u003c/dt\u003e\n  \u003cdd\u003e\n  A machine suitable for the main and workers of the cluster when your\n  model requires more computation than the standard machine can handle\n  satisfactorily.\n  \u003c/dd\u003e\n  \u003cdt\u003ecomplex_model_m\u003c/dt\u003e\n  \u003cdd\u003e\n  A machine with roughly twice the number of cores and roughly double the\n  memory of \u003ci\u003ecomplex_model_s\u003c/i\u003e.\n  \u003c/dd\u003e\n  \u003cdt\u003ecomplex_model_l\u003c/dt\u003e\n  \u003cdd\u003e\n  A machine with roughly twice the number of cores and roughly double the\n  memory of \u003ci\u003ecomplex_model_m\u003c/i\u003e.\n  \u003c/dd\u003e\n  \u003cdt\u003estandard_gpu\u003c/dt\u003e\n  \u003cdd\u003e\n  A machine equivalent to \u003ci\u003estandard\u003c/i\u003e that\n  also includes a single NVIDIA Tesla K80 GPU. See more about\n  \u003ca href=\"/ml-engine/docs/tensorflow/using-gpus\"\u003eusing GPUs to\n  train your model\u003c/a\u003e.\n  \u003c/dd\u003e\n  \u003cdt\u003ecomplex_model_m_gpu\u003c/dt\u003e\n  \u003cdd\u003e\n  A machine equivalent to \u003ci\u003ecomplex_model_m\u003c/i\u003e that also includes\n  four NVIDIA Tesla K80 GPUs.\n  \u003c/dd\u003e\n  \u003cdt\u003ecomplex_model_l_gpu\u003c/dt\u003e\n  \u003cdd\u003e\n  A machine equivalent to \u003ci\u003ecomplex_model_l\u003c/i\u003e that also includes\n  eight NVIDIA Tesla K80 GPUs.\n  \u003c/dd\u003e\n  \u003cdt\u003estandard_p100\u003c/dt\u003e\n  \u003cdd\u003e\n  A machine equivalent to \u003ci\u003estandard\u003c/i\u003e that\n  also includes a single NVIDIA Tesla P100 GPU.\n  \u003c/dd\u003e\n  \u003cdt\u003ecomplex_model_m_p100\u003c/dt\u003e\n  \u003cdd\u003e\n  A machine equivalent to \u003ci\u003ecomplex_model_m\u003c/i\u003e that also includes\n  four NVIDIA Tesla P100 GPUs.\n  \u003c/dd\u003e\n  \u003cdt\u003estandard_v100\u003c/dt\u003e\n  \u003cdd\u003e\n  A machine equivalent to \u003ci\u003estandard\u003c/i\u003e that\n  also includes a single NVIDIA Tesla V100 GPU. The availability of these\n  GPUs is in the \u003ci\u003eBeta\u003c/i\u003e launch stage.\n  \u003c/dd\u003e\n  \u003cdt\u003elarge_model_v100\u003c/dt\u003e\n  \u003cdd\u003e\n  A machine equivalent to \u003ci\u003elarge_model\u003c/i\u003e that\n  also includes a single NVIDIA Tesla V100 GPU. The availability of these\n  GPUs is in the \u003ci\u003eBeta\u003c/i\u003e launch stage.\n  \u003c/dd\u003e\n  \u003cdt\u003ecomplex_model_m_v100\u003c/dt\u003e\n  \u003cdd\u003e\n  A machine equivalent to \u003ci\u003ecomplex_model_m\u003c/i\u003e that\n  also includes four NVIDIA Tesla V100 GPUs. The availability of these\n  GPUs is in the \u003ci\u003eBeta\u003c/i\u003e launch stage.\n  \u003c/dd\u003e\n  \u003cdt\u003ecomplex_model_l_v100\u003c/dt\u003e\n  \u003cdd\u003e\n  A machine equivalent to \u003ci\u003ecomplex_model_l\u003c/i\u003e that\n  also includes eight NVIDIA Tesla V100 GPUs. The availability of these\n  GPUs is in the \u003ci\u003eBeta\u003c/i\u003e launch stage.\n  \u003c/dd\u003e\n  \u003cdt\u003ecloud_tpu\u003c/dt\u003e\n  \u003cdd\u003e\n  A TPU VM including one Cloud TPU. See more about\n  \u003ca href=\"/ml-engine/docs/tensorflow/using-tpus\"\u003eusing TPUs to train\n  your model\u003c/a\u003e.\n  \u003c/dd\u003e\n\u003c/dl\u003e\n\nYou must set this value when `scaleTier` is set to `CUSTOM`.",
           "type": "string"
         },
         "packageUris": {
@@ -1815,7 +1815,7 @@
           "type": "string"
         },
         "parameterServerType": {
-          "description": "Optional. Specifies the type of virtual machine to use for your training\njob's parameter server.\n\nThe supported values are the same as those described in the entry for\n`master_type`.\n\nThis value must be present when `scaleTier` is set to `CUSTOM` and\n`parameter_server_count` is greater than zero.",
+          "description": "Optional. Specifies the type of virtual machine to use for your training\njob's parameter server.\n\nThe supported values are the same as those described in the entry for\n`main_type`.\n\nThis value must be present when `scaleTier` is set to `CUSTOM` and\n`parameter_server_count` is greater than zero.",
           "type": "string"
         },
         "pythonModule": {
@@ -1850,7 +1850,7 @@
             "A large number of workers with many parameter servers.",
             "A single worker instance [with a\nGPU](/ml-engine/docs/tensorflow/using-gpus).",
             "A single worker instance with a\n[Cloud TPU](/ml-engine/docs/tensorflow/using-tpus).",
-            "The CUSTOM tier is not a set tier, but rather enables you to use your\nown cluster specification. When you use this tier, set values to\nconfigure your processing cluster according to these guidelines:\n\n*   You _must_ set `TrainingInput.masterType` to specify the type\n    of machine to use for your master node. This is the only required\n    setting.\n\n*   You _may_ set `TrainingInput.workerCount` to specify the number of\n    workers to use. If you specify one or more workers, you _must_ also\n    set `TrainingInput.workerType` to specify the type of machine to use\n    for your worker nodes.\n\n*   You _may_ set `TrainingInput.parameterServerCount` to specify the\n    number of parameter servers to use. If you specify one or more\n    parameter servers, you _must_ also set\n    `TrainingInput.parameterServerType` to specify the type of machine to\n    use for your parameter servers.\n\nNote that all of your workers must use the same machine type, which can\nbe different from your parameter server type and master type. Your\nparameter servers must likewise use the same machine type, which can be\ndifferent from your worker type and master type."
+            "The CUSTOM tier is not a set tier, but rather enables you to use your\nown cluster specification. When you use this tier, set values to\nconfigure your processing cluster according to these guidelines:\n\n*   You _must_ set `TrainingInput.mainType` to specify the type\n    of machine to use for your main node. This is the only required\n    setting.\n\n*   You _may_ set `TrainingInput.workerCount` to specify the number of\n    workers to use. If you specify one or more workers, you _must_ also\n    set `TrainingInput.workerType` to specify the type of machine to use\n    for your worker nodes.\n\n*   You _may_ set `TrainingInput.parameterServerCount` to specify the\n    number of parameter servers to use. If you specify one or more\n    parameter servers, you _must_ also set\n    `TrainingInput.parameterServerType` to specify the type of machine to\n    use for your parameter servers.\n\nNote that all of your workers must use the same machine type, which can\nbe different from your parameter server type and main type. Your\nparameter servers must likewise use the same machine type, which can be\ndifferent from your worker type and main type."
           ],
           "type": "string"
         },
@@ -1860,7 +1860,7 @@
           "type": "string"
         },
         "workerType": {
-          "description": "Optional. Specifies the type of virtual machine to use for your training\njob's worker nodes.\n\nThe supported values are the same as those described in the entry for\n`masterType`.\n\nThis value must be present when `scaleTier` is set to `CUSTOM` and\n`workerCount` is greater than zero.",
+          "description": "Optional. Specifies the type of virtual machine to use for your training\njob's worker nodes.\n\nThe supported values are the same as those described in the entry for\n`mainType`.\n\nThis value must be present when `scaleTier` is set to `CUSTOM` and\n`workerCount` is greater than zero.",
           "type": "string"
         }
       },
@@ -2258,4 +2258,4 @@
   "title": "Cloud Machine Learning Engine",
   "version": "v1",
   "version_module": true
-}
\ No newline at end of file
+}
diff --git a/google-api-go-generator/testdata/json-body.want b/google-api-go-generator/testdata/json-body.want
index 0a4810c..f3e30c8 100644
--- a/google-api-go-generator/testdata/json-body.want
+++ b/google-api-go-generator/testdata/json-body.want
@@ -1577,9 +1577,9 @@
 	// this field is that Cloud ML validates the path for use in training.
 	JobDir string `json:"jobDir,omitempty"`
 
-	// MasterType: Optional. Specifies the type of virtual machine to use
-	// for your training
-	// job's master worker.
+	// MainType: Optional. Specifies the type of virtual machine to use for
+	// your training
+	// job's main worker.
 	//
 	// The following types are supported:
 	//
@@ -1600,7 +1600,7 @@
 	//   </dd>
 	//   <dt>complex_model_s</dt>
 	//   <dd>
-	//   A machine suitable for the master and workers of the cluster when
+	//   A machine suitable for the main and workers of the cluster when
 	// your
 	//   model requires more computation than the standard machine can
 	// handle
@@ -1683,7 +1683,7 @@
 	// </dl>
 	//
 	// You must set this value when `scaleTier` is set to `CUSTOM`.
-	MasterType string `json:"masterType,omitempty"`
+	MainType string `json:"mainType,omitempty"`
 
 	// PackageUris: Required. The Google Cloud Storage location of the
 	// packages with
@@ -1708,7 +1708,7 @@
 	//
 	// The supported values are the same as those described in the entry
 	// for
-	// `master_type`.
+	// `main_type`.
 	//
 	// This value must be present when `scaleTier` is set to `CUSTOM`
 	// and
@@ -1763,9 +1763,8 @@
 	// to
 	// configure your processing cluster according to these guidelines:
 	//
-	// *   You _must_ set `TrainingInput.masterType` to specify the type
-	//     of machine to use for your master node. This is the only
-	// required
+	// *   You _must_ set `TrainingInput.mainType` to specify the type
+	//     of machine to use for your main node. This is the only required
 	//     setting.
 	//
 	// *   You _may_ set `TrainingInput.workerCount` to specify the number
@@ -1786,11 +1785,11 @@
 	//
 	// Note that all of your workers must use the same machine type, which
 	// can
-	// be different from your parameter server type and master type.
+	// be different from your parameter server type and main type.
 	// Your
 	// parameter servers must likewise use the same machine type, which can
 	// be
-	// different from your worker type and master type.
+	// different from your worker type and main type.
 	ScaleTier string `json:"scaleTier,omitempty"`
 
 	// WorkerCount: Optional. The number of worker replicas to use for the
@@ -1809,7 +1808,7 @@
 	//
 	// The supported values are the same as those described in the entry
 	// for
-	// `masterType`.
+	// `mainType`.
 	//
 	// This value must be present when `scaleTier` is set to `CUSTOM`
 	// and
@@ -4727,7 +4726,7 @@
 	//       "type": "string"
 	//     },
 	//     "parent": {
-	//       "description": "Required. The name of the project for which available locations are to be\nlisted (since some locations might be whitelisted for specific projects).",
+	//       "description": "Required. The name of the project for which available locations are to be\nlisted (since some locations might be restricted to specific projects).",
 	//       "location": "path",
 	//       "pattern": "^projects/[^/]+$",
 	//       "required": true,
diff --git a/transport/grpc/dial.go b/transport/grpc/dial.go
index 77ee798..19b7ddb 100644
--- a/transport/grpc/dial.go
+++ b/transport/grpc/dial.go
@@ -276,8 +276,8 @@
 	}
 
 	// Only try direct path if the user has opted in via the environment variable.
-	whitelist := strings.Split(os.Getenv("GOOGLE_CLOUD_ENABLE_DIRECT_PATH"), ",")
-	for _, api := range whitelist {
+	directPathAPIs := strings.Split(os.Getenv("GOOGLE_CLOUD_ENABLE_DIRECT_PATH"), ",")
+	for _, api := range directPathAPIs {
 		// Ignore empty string since an empty env variable splits into [""]
 		if api != "" && strings.Contains(endpoint, api) {
 			return true