# Cloud Natural Language API . documents

## Instance Methods

Finds named entities (currently proper names and common nouns) in the text

Finds entities, similar to AnalyzeEntities in the text and analyzes

Analyzes the sentiment of the provided text.

Analyzes the syntax of the text and provides sentence boundaries and

A convenience method that provides all the features that analyzeSentiment,

Classifies a document into categories.

### Method Details

`analyzeEntities(body=None, x__xgafv=None)`

    Finds named entities (currently proper names and common nouns) in the text
    along with entity types, salience, mentions for each entity, and
    other properties.

    Args:
      body: object, The request body.
        The object takes the form of:

    { # The entity analysis request message.
        "encodingType": "A String", # The encoding type used by the API to calculate offsets.
        "document": { # ################################################################ # # Required. Input document.
            #
            # Represents the input to API methods.
          "gcsContentUri": "A String", # The Google Cloud Storage URI where the file content is located.
              # This URI must be of the form: gs://bucket_name/object_name. For more
              # details, see https://cloud.google.com/storage/docs/reference-uris.
              # NOTE: Cloud Storage object versioning is not supported.
          "language": "A String", # The language of the document (if not specified, the language is
              # automatically detected). Both ISO and BCP-47 language codes are
              # accepted.<br>
              # [Language
              # Support](https://cloud.google.com/natural-language/docs/languages) lists
              # currently supported languages for each API method. If the language (either
              # specified by the caller or automatically detected) is not supported by the
              # called API method, an `INVALID_ARGUMENT` error is returned.
          "type": "A String", # Required. If the type is not set or is `TYPE_UNSPECIFIED`,
              # returns an `INVALID_ARGUMENT` error.
          "content": "A String", # The content of the input in string format.
              # Cloud audit logging exempt since it is based on user data.
        },
      }

      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # The entity analysis response message.
        "language": "A String", # The language of the text, which will be the same as the language specified
            # in the request or, if not specified, the automatically-detected language.
            # See Document.language field for more details.
        "entities": [ # The recognized entities in the input document.
          { # Represents a phrase in the text that is a known entity, such as
              # a person, an organization, or location. The API associates information, such
              # as salience and mentions, with entities.
            "mentions": [ # The mentions of this entity in the input document. The API currently
                # supports proper noun mentions.
              { # Represents a mention for an entity in the text. Currently, proper noun
                  # mentions are supported.
                "text": { # Represents an output piece of text. # The mention text.
                  "beginOffset": 42, # The API calculates the beginning offset of the content in the original
                      # document according to the EncodingType specified in the API request.
                  "content": "A String", # The content of the output text.
                },
                "type": "A String", # The type of the entity mention.
                "sentiment": { # Represents the feeling associated with the entire text or entities in # For calls to AnalyzeEntitySentiment or if
                    # AnnotateTextRequest.Features.extract_entity_sentiment is set to
                    # true, this field will contain the sentiment expressed for this mention of
                    # the entity in the provided document.
                    # the text.
                  "score": 3.14, # Sentiment score between -1.0 (negative sentiment) and 1.0
                      # (positive sentiment).
                  "magnitude": 3.14, # A non-negative number in the [0, +inf) range, which represents
                      # the absolute magnitude of sentiment regardless of score (positive or
                      # negative).
                },
              },
            ],
            "name": "A String", # The representative name for the entity.
            "type": "A String", # The entity type.
            "metadata": { # Metadata associated with the entity.
                #
                # For most entity types, the metadata is a Wikipedia URL (`wikipedia_url`)
                # and Knowledge Graph MID (`mid`), if they are available. For the metadata
                # associated with other entity types, see the Type table below.
              "a_key": "A String",
            },
            "salience": 3.14, # The salience score associated with the entity in the [0, 1.0] range.
                #
                # The salience score for an entity provides information about the
                # importance or centrality of that entity to the entire document text.
                # Scores closer to 0 are less salient, while scores closer to 1.0 are highly
                # salient.
            "sentiment": { # Represents the feeling associated with the entire text or entities in # For calls to AnalyzeEntitySentiment or if
                # AnnotateTextRequest.Features.extract_entity_sentiment is set to
                # true, this field will contain the aggregate sentiment expressed for this
                # entity in the provided document.
                # the text.
              "score": 3.14, # Sentiment score between -1.0 (negative sentiment) and 1.0
                  # (positive sentiment).
              "magnitude": 3.14, # A non-negative number in the [0, +inf) range, which represents
                  # the absolute magnitude of sentiment regardless of score (positive or
                  # negative).
            },
          },
        ],
      }

`analyzeEntitySentiment(body=None, x__xgafv=None)`

    Finds entities, similar to AnalyzeEntities in the text and analyzes
    sentiment associated with each entity and its mentions.

    Args:
      body: object, The request body.
        The object takes the form of:

    { # The entity-level sentiment analysis request message.
        "encodingType": "A String", # The encoding type used by the API to calculate offsets.
        "document": { # ################################################################ # # Required. Input document.
            #
            # Represents the input to API methods.
          "gcsContentUri": "A String", # The Google Cloud Storage URI where the file content is located.
              # This URI must be of the form: gs://bucket_name/object_name. For more
              # details, see https://cloud.google.com/storage/docs/reference-uris.
              # NOTE: Cloud Storage object versioning is not supported.
          "language": "A String", # The language of the document (if not specified, the language is
              # automatically detected). Both ISO and BCP-47 language codes are
              # accepted.<br>
              # [Language
              # Support](https://cloud.google.com/natural-language/docs/languages) lists
              # currently supported languages for each API method. If the language (either
              # specified by the caller or automatically detected) is not supported by the
              # called API method, an `INVALID_ARGUMENT` error is returned.
          "type": "A String", # Required. If the type is not set or is `TYPE_UNSPECIFIED`,
              # returns an `INVALID_ARGUMENT` error.
          "content": "A String", # The content of the input in string format.
              # Cloud audit logging exempt since it is based on user data.
        },
      }

      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # The entity-level sentiment analysis response message.
        "entities": [ # The recognized entities in the input document with associated sentiments.
          { # Represents a phrase in the text that is a known entity, such as
              # a person, an organization, or location. The API associates information, such
              # as salience and mentions, with entities.
            "mentions": [ # The mentions of this entity in the input document. The API currently
                # supports proper noun mentions.
              { # Represents a mention for an entity in the text. Currently, proper noun
                  # mentions are supported.
                "text": { # Represents an output piece of text. # The mention text.
                  "beginOffset": 42, # The API calculates the beginning offset of the content in the original
                      # document according to the EncodingType specified in the API request.
                  "content": "A String", # The content of the output text.
                },
                "type": "A String", # The type of the entity mention.
                "sentiment": { # Represents the feeling associated with the entire text or entities in # For calls to AnalyzeEntitySentiment or if
                    # AnnotateTextRequest.Features.extract_entity_sentiment is set to
                    # true, this field will contain the sentiment expressed for this mention of
                    # the entity in the provided document.
                    # the text.
                  "score": 3.14, # Sentiment score between -1.0 (negative sentiment) and 1.0
                      # (positive sentiment).
                  "magnitude": 3.14, # A non-negative number in the [0, +inf) range, which represents
                      # the absolute magnitude of sentiment regardless of score (positive or
                      # negative).
                },
              },
            ],
            "name": "A String", # The representative name for the entity.
            "type": "A String", # The entity type.
            "metadata": { # Metadata associated with the entity.
                #
                # For most entity types, the metadata is a Wikipedia URL (`wikipedia_url`)
                # and Knowledge Graph MID (`mid`), if they are available. For the metadata
                # associated with other entity types, see the Type table below.
              "a_key": "A String",
            },
            "salience": 3.14, # The salience score associated with the entity in the [0, 1.0] range.
                #
                # The salience score for an entity provides information about the
                # importance or centrality of that entity to the entire document text.
                # Scores closer to 0 are less salient, while scores closer to 1.0 are highly
                # salient.
            "sentiment": { # Represents the feeling associated with the entire text or entities in # For calls to AnalyzeEntitySentiment or if
                # AnnotateTextRequest.Features.extract_entity_sentiment is set to
                # true, this field will contain the aggregate sentiment expressed for this
                # entity in the provided document.
                # the text.
              "score": 3.14, # Sentiment score between -1.0 (negative sentiment) and 1.0
                  # (positive sentiment).
              "magnitude": 3.14, # A non-negative number in the [0, +inf) range, which represents
                  # the absolute magnitude of sentiment regardless of score (positive or
                  # negative).
            },
          },
        ],
        "language": "A String", # The language of the text, which will be the same as the language specified
            # in the request or, if not specified, the automatically-detected language.
            # See Document.language field for more details.
      }

`analyzeSentiment(body=None, x__xgafv=None)`

    Analyzes the sentiment of the provided text.

    Args:
      body: object, The request body.
        The object takes the form of:

    { # The sentiment analysis request message.
        "encodingType": "A String", # The encoding type used by the API to calculate sentence offsets.
        "document": { # ################################################################ # # Required. Input document.
            #
            # Represents the input to API methods.
          "gcsContentUri": "A String", # The Google Cloud Storage URI where the file content is located.
              # This URI must be of the form: gs://bucket_name/object_name. For more
              # details, see https://cloud.google.com/storage/docs/reference-uris.
              # NOTE: Cloud Storage object versioning is not supported.
          "language": "A String", # The language of the document (if not specified, the language is
              # automatically detected). Both ISO and BCP-47 language codes are
              # accepted.<br>
              # [Language
              # Support](https://cloud.google.com/natural-language/docs/languages) lists
              # currently supported languages for each API method. If the language (either
              # specified by the caller or automatically detected) is not supported by the
              # called API method, an `INVALID_ARGUMENT` error is returned.
          "type": "A String", # Required. If the type is not set or is `TYPE_UNSPECIFIED`,
              # returns an `INVALID_ARGUMENT` error.
          "content": "A String", # The content of the input in string format.
              # Cloud audit logging exempt since it is based on user data.
        },
      }

      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # The sentiment analysis response message.
        "sentences": [ # The sentiment for all the sentences in the document.
          { # Represents a sentence in the input document.
            "text": { # Represents an output piece of text. # The sentence text.
              "beginOffset": 42, # The API calculates the beginning offset of the content in the original
                  # document according to the EncodingType specified in the API request.
              "content": "A String", # The content of the output text.
            },
            "sentiment": { # Represents the feeling associated with the entire text or entities in # For calls to AnalyzeSentiment or if
                # AnnotateTextRequest.Features.extract_document_sentiment is set to
                # true, this field will contain the sentiment for the sentence.
                # the text.
              "score": 3.14, # Sentiment score between -1.0 (negative sentiment) and 1.0
                  # (positive sentiment).
              "magnitude": 3.14, # A non-negative number in the [0, +inf) range, which represents
                  # the absolute magnitude of sentiment regardless of score (positive or
                  # negative).
            },
          },
        ],
        "documentSentiment": { # Represents the feeling associated with the entire text or entities in # The overall sentiment of the input document.
            # the text.
          "score": 3.14, # Sentiment score between -1.0 (negative sentiment) and 1.0
              # (positive sentiment).
          "magnitude": 3.14, # A non-negative number in the [0, +inf) range, which represents
              # the absolute magnitude of sentiment regardless of score (positive or
              # negative).
        },
        "language": "A String", # The language of the text, which will be the same as the language specified
            # in the request or, if not specified, the automatically-detected language.
            # See Document.language field for more details.
      }

`analyzeSyntax(body=None, x__xgafv=None)`

    Analyzes the syntax of the text and provides sentence boundaries and
    tokenization along with part of speech tags, dependency trees, and other
    properties.

    Args:
      body: object, The request body.
        The object takes the form of:

    { # The syntax analysis request message.
        "encodingType": "A String", # The encoding type used by the API to calculate offsets.
        "document": { # ################################################################ # # Required. Input document.
            #
            # Represents the input to API methods.
          "gcsContentUri": "A String", # The Google Cloud Storage URI where the file content is located.
              # This URI must be of the form: gs://bucket_name/object_name. For more
              # details, see https://cloud.google.com/storage/docs/reference-uris.
              # NOTE: Cloud Storage object versioning is not supported.
          "language": "A String", # The language of the document (if not specified, the language is
              # automatically detected). Both ISO and BCP-47 language codes are
              # accepted.<br>
              # [Language
              # Support](https://cloud.google.com/natural-language/docs/languages) lists
              # currently supported languages for each API method. If the language (either
              # specified by the caller or automatically detected) is not supported by the
              # called API method, an `INVALID_ARGUMENT` error is returned.
          "type": "A String", # Required. If the type is not set or is `TYPE_UNSPECIFIED`,
              # returns an `INVALID_ARGUMENT` error.
          "content": "A String", # The content of the input in string format.
              # Cloud audit logging exempt since it is based on user data.
        },
      }

      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # The syntax analysis response message.
        "language": "A String", # The language of the text, which will be the same as the language specified
            # in the request or, if not specified, the automatically-detected language.
            # See Document.language field for more details.
        "sentences": [ # Sentences in the input document.
          { # Represents a sentence in the input document.
            "text": { # Represents an output piece of text. # The sentence text.
              "beginOffset": 42, # The API calculates the beginning offset of the content in the original
                  # document according to the EncodingType specified in the API request.
              "content": "A String", # The content of the output text.
            },
            "sentiment": { # Represents the feeling associated with the entire text or entities in # For calls to AnalyzeSentiment or if
                # AnnotateTextRequest.Features.extract_document_sentiment is set to
                # true, this field will contain the sentiment for the sentence.
                # the text.
              "score": 3.14, # Sentiment score between -1.0 (negative sentiment) and 1.0
                  # (positive sentiment).
              "magnitude": 3.14, # A non-negative number in the [0, +inf) range, which represents
                  # the absolute magnitude of sentiment regardless of score (positive or
                  # negative).
            },
          },
        ],
        "tokens": [ # Tokens, along with their syntactic information, in the input document.
          { # Represents the smallest syntactic building block of the text.
            "text": { # Represents an output piece of text. # The token text.
              "beginOffset": 42, # The API calculates the beginning offset of the content in the original
                  # document according to the EncodingType specified in the API request.
              "content": "A String", # The content of the output text.
            },
            "dependencyEdge": { # Represents dependency parse tree information for a token. (For more # Dependency tree parse for this token.
                # information on dependency labels, see
                # http://www.aclweb.org/anthology/P13-2017
              "headTokenIndex": 42, # Represents the head of this token in the dependency tree.
                  # This is the index of the token which has an arc going to this token.
                  # The index is the position of the token in the array of tokens returned
                  # by the API method. If this token is a root token, then the
                  # `head_token_index` is its own index.
              "label": "A String", # The parse label for the token.
            },
            "lemma": "A String", # [Lemma](https://en.wikipedia.org/wiki/Lemma_%28morphology%29) of the token.
            "partOfSpeech": { # Represents part of speech information for a token. Parts of speech # Parts of speech tag for this token.
                # are as defined in
                # http://www.lrec-conf.org/proceedings/lrec2012/pdf/274_Paper.pdf
              "form": "A String", # The grammatical form.
              "number": "A String", # The grammatical number.
              "voice": "A String", # The grammatical voice.
              "aspect": "A String", # The grammatical aspect.
              "mood": "A String", # The grammatical mood.
              "tag": "A String", # The part of speech tag.
              "gender": "A String", # The grammatical gender.
              "person": "A String", # The grammatical person.
              "proper": "A String", # The grammatical properness.
              "case": "A String", # The grammatical case.
              "tense": "A String", # The grammatical tense.
              "reciprocity": "A String", # The grammatical reciprocity.
            },
          },
        ],
      }

`annotateText(body=None, x__xgafv=None)`

    A convenience method that provides all the features that analyzeSentiment,
    analyzeEntities, and analyzeSyntax provide in one call.

    Args:
      body: object, The request body.
        The object takes the form of:

    { # The request message for the text annotation API, which can perform multiple
          # analysis types (sentiment, entities, and syntax) in one call.
        "encodingType": "A String", # The encoding type used by the API to calculate offsets.
        "document": { # ################################################################ # # Required. Input document.
            #
            # Represents the input to API methods.
          "gcsContentUri": "A String", # The Google Cloud Storage URI where the file content is located.
              # This URI must be of the form: gs://bucket_name/object_name. For more
              # details, see https://cloud.google.com/storage/docs/reference-uris.
              # NOTE: Cloud Storage object versioning is not supported.
          "language": "A String", # The language of the document (if not specified, the language is
              # automatically detected). Both ISO and BCP-47 language codes are
              # accepted.<br>
              # [Language
              # Support](https://cloud.google.com/natural-language/docs/languages) lists
              # currently supported languages for each API method. If the language (either
              # specified by the caller or automatically detected) is not supported by the
              # called API method, an `INVALID_ARGUMENT` error is returned.
          "type": "A String", # Required. If the type is not set or is `TYPE_UNSPECIFIED`,
              # returns an `INVALID_ARGUMENT` error.
          "content": "A String", # The content of the input in string format.
              # Cloud audit logging exempt since it is based on user data.
        },
        "features": { # All available features for sentiment, syntax, and semantic analysis. # Required. The enabled features.
            # Setting each one to true will enable that specific analysis for the input.
          "classifyText": True or False, # Classify the full document into categories.
          "extractSyntax": True or False, # Extract syntax information.
          "extractDocumentSentiment": True or False, # Extract document-level sentiment.
          "extractEntitySentiment": True or False, # Extract entities and their associated sentiment.
          "extractEntities": True or False, # Extract entities.
        },
      }

      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # The text annotations response message.
        "sentences": [ # Sentences in the input document. Populated if the user enables
            # AnnotateTextRequest.Features.extract_syntax.
          { # Represents a sentence in the input document.
            "text": { # Represents an output piece of text. # The sentence text.
              "beginOffset": 42, # The API calculates the beginning offset of the content in the original
                  # document according to the EncodingType specified in the API request.
              "content": "A String", # The content of the output text.
            },
            "sentiment": { # Represents the feeling associated with the entire text or entities in # For calls to AnalyzeSentiment or if
                # AnnotateTextRequest.Features.extract_document_sentiment is set to
                # true, this field will contain the sentiment for the sentence.
                # the text.
              "score": 3.14, # Sentiment score between -1.0 (negative sentiment) and 1.0
                  # (positive sentiment).
              "magnitude": 3.14, # A non-negative number in the [0, +inf) range, which represents
                  # the absolute magnitude of sentiment regardless of score (positive or
                  # negative).
            },
          },
        ],
        "tokens": [ # Tokens, along with their syntactic information, in the input document.
            # Populated if the user enables
            # AnnotateTextRequest.Features.extract_syntax.
          { # Represents the smallest syntactic building block of the text.
            "text": { # Represents an output piece of text. # The token text.
              "beginOffset": 42, # The API calculates the beginning offset of the content in the original
                  # document according to the EncodingType specified in the API request.
              "content": "A String", # The content of the output text.
            },
            "dependencyEdge": { # Represents dependency parse tree information for a token. (For more # Dependency tree parse for this token.
                # information on dependency labels, see
                # http://www.aclweb.org/anthology/P13-2017
              "headTokenIndex": 42, # Represents the head of this token in the dependency tree.
                  # This is the index of the token which has an arc going to this token.
                  # The index is the position of the token in the array of tokens returned
                  # by the API method. If this token is a root token, then the
                  # `head_token_index` is its own index.
              "label": "A String", # The parse label for the token.
            },
            "lemma": "A String", # [Lemma](https://en.wikipedia.org/wiki/Lemma_%28morphology%29) of the token.
            "partOfSpeech": { # Represents part of speech information for a token. Parts of speech # Parts of speech tag for this token.
                # are as defined in
                # http://www.lrec-conf.org/proceedings/lrec2012/pdf/274_Paper.pdf
              "form": "A String", # The grammatical form.
              "number": "A String", # The grammatical number.
              "voice": "A String", # The grammatical voice.
              "aspect": "A String", # The grammatical aspect.
              "mood": "A String", # The grammatical mood.
              "tag": "A String", # The part of speech tag.
              "gender": "A String", # The grammatical gender.
              "person": "A String", # The grammatical person.
              "proper": "A String", # The grammatical properness.
              "case": "A String", # The grammatical case.
              "tense": "A String", # The grammatical tense.
              "reciprocity": "A String", # The grammatical reciprocity.
            },
          },
        ],
        "categories": [ # Categories identified in the input document.
          { # Represents a category returned from the text classifier.
            "confidence": 3.14, # The classifier's confidence of the category. Number represents how certain
                # the classifier is that this category represents the given text.
            "name": "A String", # The name of the category representing the document, from the [predefined
                # taxonomy](https://cloud.google.com/natural-language/docs/categories).
          },
        ],
        "documentSentiment": { # Represents the feeling associated with the entire text or entities in # The overall sentiment for the document. Populated if the user enables
            # AnnotateTextRequest.Features.extract_document_sentiment.
            # the text.
          "score": 3.14, # Sentiment score between -1.0 (negative sentiment) and 1.0
              # (positive sentiment).
          "magnitude": 3.14, # A non-negative number in the [0, +inf) range, which represents
              # the absolute magnitude of sentiment regardless of score (positive or
              # negative).
        },
        "language": "A String", # The language of the text, which will be the same as the language specified
            # in the request or, if not specified, the automatically-detected language.
            # See Document.language field for more details.
        "entities": [ # Entities, along with their semantic information, in the input document.
            # Populated if the user enables
            # AnnotateTextRequest.Features.extract_entities.
          { # Represents a phrase in the text that is a known entity, such as
              # a person, an organization, or location. The API associates information, such
              # as salience and mentions, with entities.
            "mentions": [ # The mentions of this entity in the input document. The API currently
                # supports proper noun mentions.
              { # Represents a mention for an entity in the text. Currently, proper noun
                  # mentions are supported.
                "text": { # Represents an output piece of text. # The mention text.
                  "beginOffset": 42, # The API calculates the beginning offset of the content in the original
                      # document according to the EncodingType specified in the API request.
                  "content": "A String", # The content of the output text.
                },
                "type": "A String", # The type of the entity mention.
                "sentiment": { # Represents the feeling associated with the entire text or entities in # For calls to AnalyzeEntitySentiment or if
                    # AnnotateTextRequest.Features.extract_entity_sentiment is set to
                    # true, this field will contain the sentiment expressed for this mention of
                    # the entity in the provided document.
                    # the text.
                  "score": 3.14, # Sentiment score between -1.0 (negative sentiment) and 1.0
                      # (positive sentiment).
                  "magnitude": 3.14, # A non-negative number in the [0, +inf) range, which represents
                      # the absolute magnitude of sentiment regardless of score (positive or
                      # negative).
                },
              },
            ],
            "name": "A String", # The representative name for the entity.
            "type": "A String", # The entity type.
            "metadata": { # Metadata associated with the entity.
                #
                # For most entity types, the metadata is a Wikipedia URL (`wikipedia_url`)
                # and Knowledge Graph MID (`mid`), if they are available. For the metadata
                # associated with other entity types, see the Type table below.
              "a_key": "A String",
            },
            "salience": 3.14, # The salience score associated with the entity in the [0, 1.0] range.
                #
                # The salience score for an entity provides information about the
                # importance or centrality of that entity to the entire document text.
                # Scores closer to 0 are less salient, while scores closer to 1.0 are highly
                # salient.
            "sentiment": { # Represents the feeling associated with the entire text or entities in # For calls to AnalyzeEntitySentiment or if
                # AnnotateTextRequest.Features.extract_entity_sentiment is set to
                # true, this field will contain the aggregate sentiment expressed for this
                # entity in the provided document.
                # the text.
              "score": 3.14, # Sentiment score between -1.0 (negative sentiment) and 1.0
                  # (positive sentiment).
              "magnitude": 3.14, # A non-negative number in the [0, +inf) range, which represents
                  # the absolute magnitude of sentiment regardless of score (positive or
                  # negative).
            },
          },
        ],
      }

`classifyText(body=None, x__xgafv=None)`

    Classifies a document into categories.

    Args:
      body: object, The request body.
        The object takes the form of:

    { # The document classification request message.
        "document": { # ################################################################ # # Required. Input document.
            #
            # Represents the input to API methods.
          "gcsContentUri": "A String", # The Google Cloud Storage URI where the file content is located.
              # This URI must be of the form: gs://bucket_name/object_name. For more
              # details, see https://cloud.google.com/storage/docs/reference-uris.
              # NOTE: Cloud Storage object versioning is not supported.
          "language": "A String", # The language of the document (if not specified, the language is
              # automatically detected). Both ISO and BCP-47 language codes are
              # accepted.<br>
              # [Language
              # Support](https://cloud.google.com/natural-language/docs/languages) lists
              # currently supported languages for each API method. If the language (either
              # specified by the caller or automatically detected) is not supported by the
              # called API method, an `INVALID_ARGUMENT` error is returned.
          "type": "A String", # Required. If the type is not set or is `TYPE_UNSPECIFIED`,
              # returns an `INVALID_ARGUMENT` error.
          "content": "A String", # The content of the input in string format.
              # Cloud audit logging exempt since it is based on user data.
        },
      }

      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # The document classification response message.
        "categories": [ # Categories representing the input document.
          { # Represents a category returned from the text classifier.
            "confidence": 3.14, # The classifier's confidence of the category. Number represents how certain
                # the classifier is that this category represents the given text.
            "name": "A String", # The name of the category representing the document, from the [predefined
                # taxonomy](https://cloud.google.com/natural-language/docs/categories).
          },
        ],
      }