lint

Signed-off-by: Future-Outlier <[email protected]>

docs/user_guide/data_types_and_io/accessing_attributes.md

@@ -23,7 +23,7 @@ To begin, import the required dependencies and define a common task for subseque
 
 ```{literalinclude} /examples/data_types_and_io/data_types_and_io/attribute_access.py
 :caption: data_types_and_io/attribute_access.py
-:lines: 1-10
+:lines: 1-9
 ```
 
 ## List
@@ -35,38 +35,38 @@ Flyte currently does not support output promise access through list slicing.
 
 ```{literalinclude} /examples/data_types_and_io/data_types_and_io/attribute_access.py
 :caption: data_types_and_io/attribute_access.py
-:lines: 14-23
+:lines: 13-22
 ```
 
 ## Dictionary
 Access the output dictionary by specifying the key.
 
 ```{literalinclude} /examples/data_types_and_io/data_types_and_io/attribute_access.py
 :caption: data_types_and_io/attribute_access.py
-:lines: 27-35
+:lines: 26-34
 ```
 
 ## Data class
 Directly access an attribute of a dataclass.
 
 ```{literalinclude} /examples/data_types_and_io/data_types_and_io/attribute_access.py
 :caption: data_types_and_io/attribute_access.py
-:lines: 39-53
+:lines: 38-51
 ```
 
 ## Complex type
 Combinations of list, dict and dataclass also work effectively.
 
 ```{literalinclude} /examples/data_types_and_io/data_types_and_io/attribute_access.py
 :caption: data_types_and_io/attribute_access.py
-:lines: 57-80
+:lines: 55-78
 ```
 
 You can run all the workflows locally as follows:
 
 ```{literalinclude} /examples/data_types_and_io/data_types_and_io/attribute_access.py
 :caption: data_types_and_io/attribute_access.py
-:lines: 84-88
+:lines: 82-86
 ```
 
 ## Failure scenario

docs/user_guide/data_types_and_io/index.md

@@ -114,7 +114,7 @@ Here's a breakdown of these mappings:
       - Use ``pyspark.DataFrame`` as a type hint.
     * - ``pydantic.BaseModel``
       - ``Map``
-      - To utilize the type, install the ``flytekitplugins-pydantic`` plugin.
+      - To utilize the type, install the ``pydantic>2`` module.
       - Use ``pydantic.BaseModel`` as a type hint.
     * - ``torch.Tensor`` / ``torch.nn.Module``
       - File
@@ -144,7 +144,7 @@ flytefile
 flytedirectory
 structureddataset
 dataclass
-pydantic
+pydantic_basemodel
 accessing_attributes
 pytorch_type
 enum_type

docs/user_guide/data_types_and_io/pydantic.md → docs/user_guide/data_types_and_io/pydantic_basemodel.md

@@ -1,26 +1,27 @@
-(pydantic)=
+(pydantic_basemodel)=
 
 # Pydantic BaseModel
 
 ```{eval-rst}
 .. tags:: Basic
 ```
 
-When you've multiple values that you want to send across Flyte entities, and you want them to have, you can use a `pydantic.BaseModel`.
-Note:
-You can put Dataclass and FlyteTypes (FlyteFile, FlyteDirectory, FlyteSchema, and StructuredDataset) in a pydantic BaseModel.
+When you have multiple values that you want to send across Flyte entities, and you want them to have, you can use a `pydantic.BaseModel`.
 
 :::{important}
 Pydantic BaseModel V2 only works when you are using flytekit version >= v1.14.0.
 :::
 
 :::{important}
-If you're using Flytekit version >= v1.14.0 and you want to produce protobuf struct literal for pydantic basemodels,
+If you're using Flytekit version >= v1.14.0 and you want to produce protobuf struct literal for Pydantic BaseModels,
 you can set environment variable  `FLYTE_USE_OLD_DC_FORMAT` to `true`.
 
-For more details, you can refer the MSGPACK IDL RFC: https://github.com/flyteorg/flyte/blob/master/rfc/system/5741-binary-idl-with-message-pack.md
+For more details, you can refer the MESSAGEPACK IDL RFC: https://github.com/flyteorg/flyte/blob/master/rfc/system/5741-binary-idl-with-message-pack.md
 :::
 
+```{note}
+You can put Dataclass and FlyteTypes (FlyteFile, FlyteDirectory, FlyteSchema, and StructuredDataset) in a pydantic BaseModel.
+```
 
 ```{note}
 To clone and run the example code on this page, see the [Flytesnacks repo][flytesnacks].
@@ -40,14 +41,14 @@ Build your custom image with ImageSpec:
 ```
 
 ## Python types
-We define a `dataclass` with `int`, `str` and `dict` as the data types.
+We define a `pydantic basemodel` with `int`, `str` and `dict` as the data types.
 
 ```{literalinclude} /examples/data_types_and_io/data_types_and_io/pydantic_basemodel.py
 :caption: data_types_and_io/pydantic_basemodel.py
 :pyobject: Datum
 ```
 
-You can send a `dataclass` between different tasks written in various languages, and input it through the Flyte console as raw JSON.
+You can send a `pydantic basemodel` between different tasks written in various languages, and input it through the Flyte console as raw JSON.
 
 :::{note}
 All variables in a data class should be **annotated with their type**. Failure to do should will result in an error.