code: def generate_pydantic_schema_for_fields(
      fields: List[
          Union["AllowedFieldTypes", "AllowedRemoteFieldTypes"]
      ],
      name: Optional[str] = "FieldsSchema",
  ) -> typing.Type[BaseModel]:
      """Generates a `pydantic.BaseModel` schema from a list of `AllowedFieldTypes` or `AllowedRemoteFieldTypes`
      objects to validate the fields of a `FeedbackDataset` or `RemoteFeedbackDataset` object, respectively,
      before inserting them.
  
      Args:
          fields: the list of `AllowedFieldTypes` or `AllowedRemoteFieldTypes` objects to generate the schema from.
          name: the name of the `pydantic.BaseModel` schema to generate. Defaults to "FieldsSchema".
  
      Returns:
          A `pydantic.BaseModel` schema to validate the fields of a `FeedbackDataset` or `RemoteFeedbackDataset`
          object before inserting them.
  
      Raises:
          ValueError: if one of the fields has an unsupported type.
  
      Examples:
          >>> from argilla.client.feedback.schemas.fields import TextField
          >>> from argilla.client.feedback.dataset.helpers import generate_pydantic_schema_for_fields
          >>> fields = [
          ...     TextField(name="text", required=True),
          ...     TextField(name="label", required=True),
          ... ]
          >>> FieldsSchema = generate_pydantic_schema_for_fields(fields)
          >>> FieldsSchema(text="Hello", label="World")
          FieldsSchema(text='Hello', label='World')
      """
      fields_schema = {}
      for field in fields:
          if field.type not in FIELD_TYPE_TO_PYTHON_TYPE.keys():
              raise ValueError(
                  f"Field {field.name} has an unsupported type: {field.type}, for the moment only the"
                  f" following types are supported: {list(FIELD_TYPE_TO_PYTHON_TYPE.keys())}"
              )
          fields_schema.update(
              {
                  field.name: (
                      FIELD_TYPE_TO_PYTHON_TYPE[field.type],
                      ... if field.required else None,
                  )
              }
          )
      return create_model(name, **fields_schema)
  
imports: import typing
  from typing import Any, Dict, List, Optional, Type, Union
  import httpx
  from argilla.client.feedback.constants import FIELD_TYPE_TO_PYTHON_TYPE
  from argilla.client.feedback.dataset.base import FeedbackDatasetBase
  from argilla.client.feedback.schemas import FeedbackRecord
  from argilla.client.feedback.schemas.enums import MetadataPropertyTypes
  from argilla.client.feedback.schemas.types import AllowedFieldTypes, AllowedMetadataPropertyTypes, AllowedQuestionTypes
  from argilla.client.sdk.v1.datasets import api
  from argilla.client.sdk.v1.datasets.models import FeedbackDatasetModel
  from argilla.client.singleton import active_client
  from argilla.client.workspaces import Workspace
  from argilla.pydantic_v1 import BaseModel, Extra, ValidationError, create_model
label_desc: Use image node_id 9 for calling a global function with example usage: generate_pydantic_schema_for_fields(fields, name) and returns: create_model