chore: update generated docs (#1115)

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Source-Link: https://github.com/googleapis/discovery-artifact-manager/commit/0bed8bdae25d545e796cfcdd7a9bfffff11e69da
Source-Link: https://github.com/googleapis/synthtool/commit/c2de32114ec484aa708d32012d1fa8d75232daf5
diff --git a/docs/dyn/healthcare_v1beta1.projects.locations.services.nlp.html b/docs/dyn/healthcare_v1beta1.projects.locations.services.nlp.html
index 4c1127c..a01ada9 100644
--- a/docs/dyn/healthcare_v1beta1.projects.locations.services.nlp.html
+++ b/docs/dyn/healthcare_v1beta1.projects.locations.services.nlp.html
@@ -105,15 +105,14 @@
     { # Includes recognized entity mentions and relationships between them.
     "relationships": [ # relationships contains all the binary relationships that were identified between entity mentions within the provided document.
       { # Defines directed relationship from one entity mention to another.
-        "objectId": "A String", # object_id is the id of the object entity mention.
-        "subjectId": "A String", # subject_id is the id of the subject entity mention.
         "confidence": 3.14, # The model's confidence in this annotation. A number between 0 and 1.
+        "subjectId": "A String", # subject_id is the id of the subject entity mention.
+        "objectId": "A String", # object_id is the id of the object entity mention.
       },
     ],
     "entityMentions": [ # entity_mentions contains all the annotated medical entities that were were mentioned in the provided document.
       { # An entity mention in the document.
-        "confidence": 3.14, # The model's confidence in this entity mention annotation. A number between 0 and 1.
-        "subject": { # A feature of an entity mention. # The subject this entity mention relates to. Its value is one of: PATIENT, FAMILY_MEMBER, OTHER
+        "certaintyAssessment": { # A feature of an entity mention. # The certainty assessment of the entity mention. Its value is one of: LIKELY, SOMEWHAT_LIKELY, UNCERTAIN, SOMEWHAT_UNLIKELY, UNLIKELY, CONDITIONAL
           "value": "A String", # The value of this feature annotation. Its range depends on the type of the feature.
           "confidence": 3.14, # The model's confidence in this feature annotation. A number between 0 and 1.
         },
@@ -121,30 +120,31 @@
           "value": "A String", # The value of this feature annotation. Its range depends on the type of the feature.
           "confidence": 3.14, # The model's confidence in this feature annotation. A number between 0 and 1.
         },
-        "certaintyAssessment": { # A feature of an entity mention. # The certainty assessment of the entity mention. Its value is one of: LIKELY, SOMEWHAT_LIKELY, UNCERTAIN, SOMEWHAT_UNLIKELY, UNLIKELY, CONDITIONAL
+        "subject": { # A feature of an entity mention. # The subject this entity mention relates to. Its value is one of: PATIENT, FAMILY_MEMBER, OTHER
           "value": "A String", # The value of this feature annotation. Its range depends on the type of the feature.
           "confidence": 3.14, # The model's confidence in this feature annotation. A number between 0 and 1.
         },
-        "mentionId": "A String", # mention_id uniquely identifies each entity mention in a single response.
-        "text": { # A span of text in the provided document. # text is the location of the entity mention in the document.
-          "content": "A String", # The original text contained in this span.
-          "beginOffset": 42, # The unicode codepoint index of the beginning of this span.
-        },
         "linkedEntities": [ # linked_entities are candidate ontological concepts that this entity mention may refer to. They are sorted by decreasing confidence.it
           { # EntityMentions can be linked to multiple entities using a LinkedEntity message lets us add other fields, e.g. confidence.
             "entityId": "A String", # entity_id is a concept unique identifier. These are prefixed by a string that identifies the entity coding system, followed by the unique identifier within that system. For example, "UMLS/C0000970". This also supports ad hoc entities, which are formed by normalizing entity mention content.
           },
         ],
+        "text": { # A span of text in the provided document. # text is the location of the entity mention in the document.
+          "beginOffset": 42, # The unicode codepoint index of the beginning of this span.
+          "content": "A String", # The original text contained in this span.
+        },
         "type": "A String", # The semantic type of the entity: UNKNOWN_ENTITY_TYPE, ALONE, ANATOMICAL_STRUCTURE, ASSISTED_LIVING, BF_RESULT, BM_RESULT, BM_UNIT, BM_VALUE, BODY_FUNCTION, BODY_MEASUREMENT, COMPLIANT, DOESNOT_FOLLOWUP, FAMILY, FOLLOWSUP, LABORATORY_DATA, LAB_RESULT, LAB_UNIT, LAB_VALUE, MEDICAL_DEVICE, MEDICINE, MED_DOSE, MED_DURATION, MED_FORM, MED_FREQUENCY, MED_ROUTE, MED_STATUS, MED_STRENGTH, MED_TOTALDOSE, MED_UNIT, NON_COMPLIANT, OTHER_LIVINGSTATUS, PROBLEM, PROCEDURE, PROCEDURE_RESULT, PROC_METHOD, REASON_FOR_NONCOMPLIANCE, SEVERITY, SUBSTANCE_ABUSE, UNCLEAR_FOLLOWUP.
+        "mentionId": "A String", # mention_id uniquely identifies each entity mention in a single response.
+        "confidence": 3.14, # The model's confidence in this entity mention annotation. A number between 0 and 1.
       },
     ],
     "entities": [ # The union of all the candidate entities that the entity_mentions in this response could link to. These are UMLS concepts or normalized mention content.
       { # The candidate entities that an entity mention could link to.
-        "entityId": "A String", # entity_id is a first class field entity_id uniquely identifies this concept and its meta-vocabulary. For example, "UMLS/C0000970".
-        "preferredTerm": "A String", # preferred_term is the preferred term for this concept. For example, "Acetaminophen". For ad hoc entities formed by normalization, this is the most popular unnormalized string.
         "vocabularyCodes": [ # Vocabulary codes are first-class fields and differentiated from the concept unique identifier (entity_id). vocabulary_codes contains the representation of this concept in particular vocabularies, such as ICD-10, SNOMED-CT and RxNORM. These are prefixed by the name of the vocabulary, followed by the unique code within that vocabulary. For example, "RXNORM/A10334543".
           "A String",
         ],
+        "entityId": "A String", # entity_id is a first class field entity_id uniquely identifies this concept and its meta-vocabulary. For example, "UMLS/C0000970".
+        "preferredTerm": "A String", # preferred_term is the preferred term for this concept. For example, "Acetaminophen". For ad hoc entities formed by normalization, this is the most popular unnormalized string.
       },
     ],
   }</pre>