[API] Updates ml.start_trained_model_deployment, adds body

Author: picandocodigo

elasticsearch-api/lib/elasticsearch/api/actions/machine_learning/start_trained_model_deployment.rb

@@ -15,33 +15,49 @@
 # specific language governing permissions and limitations
 # under the License.
 #
-# Auto generated from build hash f284cc16f4d4b4289bc679aa1529bb504190fe80
-# @see https://github.com/elastic/elasticsearch/tree/main/rest-api-spec
+# Auto generated from commit f284cc16f4d4b4289bc679aa1529bb504190fe80
+# @see https://github.com/elastic/elasticsearch-specification
 #
 module Elasticsearch
   module API
     module MachineLearning
       module Actions
         # Start a trained model deployment.
+        # It allocates the model to every machine learning node.
         #
-        # @option arguments [String] :model_id The unique identifier of the trained model. (*Required*)
-        # @option arguments [String] :cache_size A byte-size value for configuring the inference cache size. For example, 20mb.
-        # @option arguments [String] :deployment_id The Id of the new deployment. Defaults to the model_id if not set.
-        # @option arguments [Integer] :number_of_allocations The total number of allocations this model is assigned across machine learning nodes.
-        # @option arguments [Integer] :threads_per_allocation The number of threads used by each model allocation during inference.
+        # @option arguments [String] :model_id The unique identifier of the trained model. Currently, only PyTorch models are supported. (*Required*)
+        # @option arguments [Integer, String] :cache_size The inference cache size (in memory outside the JVM heap) per node for the model.
+        #  The default value is the same size as the +model_size_bytes+. To disable the cache,
+        #  +0b+ can be provided.
+        # @option arguments [String] :deployment_id A unique identifier for the deployment of the model.
+        # @option arguments [Integer] :number_of_allocations The number of model allocations on each node where the model is deployed.
+        #  All allocations on a node share the same copy of the model in memory but use
+        #  a separate set of threads to evaluate the model.
+        #  Increasing this value generally increases the throughput.
+        #  If this setting is greater than the number of hardware threads
+        #  it will automatically be changed to a value less than the number of hardware threads.
+        #  If adaptive_allocations is enabled, do not set this value, because it’s automatically set. Server default: 1.
         # @option arguments [String] :priority The deployment priority.
-        # @option arguments [Integer] :queue_capacity Controls how many inference requests are allowed in the queue at a time.
-        # @option arguments [Time] :timeout Controls the amount of time to wait for the model to deploy.
-        # @option arguments [String] :wait_for The allocation status for which to wait (options: starting, started, fully_allocated)
+        # @option arguments [Integer] :queue_capacity Specifies the number of inference requests that are allowed in the queue. After the number of requests exceeds
+        #  this value, new requests are rejected with a 429 error. Server default: 1024.
+        # @option arguments [Integer] :threads_per_allocation Sets the number of threads used by each model allocation during inference. This generally increases
+        #  the inference speed. The inference process is a compute-bound process; any number
+        #  greater than the number of available hardware threads on the machine does not increase the
+        #  inference speed. If this setting is greater than the number of hardware threads
+        #  it will automatically be changed to a value less than the number of hardware threads. Server default: 1.
+        # @option arguments [Time] :timeout Specifies the amount of time to wait for the model to deploy. Server default: 20s.
+        # @option arguments [String] :wait_for Specifies the allocation status to wait for before returning. Server default: started.
         # @option arguments [Hash] :headers Custom HTTP headers
+        # @option arguments [Hash] :body request body
         #
-        # @see https://www.elastic.co/guide/en/elasticsearch/reference/current/start-trained-model-deployment.html
+        # @see https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-ml-start-trained-model-deployment
         #
         def start_trained_model_deployment(arguments = {})
           request_opts = { endpoint: arguments[:endpoint] || 'ml.start_trained_model_deployment' }
 
-          defined_params = [:model_id].each_with_object({}) do |variable, set_variables|
+          defined_params = [:model_id].inject({}) do |set_variables, variable|
             set_variables[variable] = arguments[variable] if arguments.key?(variable)
+            set_variables
           end
           request_opts[:defined_params] = defined_params unless defined_params.empty?
 
@@ -50,7 +66,7 @@ def start_trained_model_deployment(arguments = {})
           arguments = arguments.clone
           headers = arguments.delete(:headers) || {}
 
-          body = nil
+          body = arguments.delete(:body)
 
           _model_id = arguments.delete(:model_id)