I haven't found a way to make the Globals class use the extended definition of the table

You can change the type of a field in a subclass if you declare the field again using the desired type.

Also, simply adding new fields to the subclass does not seem to work

It looks like you are setting instance attributes in the __init__() method, but fields are declared as class attributes.

This example shows a way to add a calculated field records_by_id to ExtendedTable and use ExtendedTable in ExtendedGlobals:

# File: extensions.py
import json
from typing import Any, Dict, List, Optional

from pydantic import Field, validator

from datamodel import Globals, Record, Table


class ExtendedTable(Table):
    # New fields are declared as class attributes not as instance attributes inside the __init__()
    # Calculated fields usually have a default value or default factory so that you don't have to provide a value
    # I prefer a default_factory for mutable values
    records_by_id: Dict[int, Record] = Field(default_factory=dict)

    # A validator can populate a calculated field
    # Use always=True to run the validator even if a value is not supplied and the default value is used
    @validator("records_by_id", always=True)
    def _calculate_records_by_id(
        cls, value: Dict[int, Record], values: Dict[str, Any]
    ) -> Dict[int, Record]:
        records: Optional[List[Record]] = values.get("records")
        if records is None:
            # The records field was not valid
            # Return value or raise a ValueError instead if you want
            return value
        return {record.id: record for record in records}


class ExtendedGlobals(Globals):
    # You can change the type of a field in a subclass if you declare the field again
    table: ExtendedTable


if __name__ == "__main__":
    records = """
    {
        "table": {
            "records": [{"id": 0, "name": "A"}, {"id": 1, "name": "B"}]
        }
    }
    """

    content = json.loads(records)
    extended_globals = ExtendedGlobals.parse_obj(content)
    print(repr(extended_globals))

Output:

ExtendedGlobals(table=ExtendedTable(records=[Record(id=0, name='A'), Record(id=1, name='B')], records_by_id={0: Record(id=0, name='A'), 1: Record(id=1, name='B')}))

In Pydantic 2, you can use MyModel.model_validate(my_dict) to generate a model from a dictionary. According to the documentation –

this is very similar to the __init__ method of the model, except it takes a dict rather than keyword arguments.

If you're Pydantic 1, the method is parse_obj instead.

From the datamodel-code-generator "official documentation" mentioned on the page you reference, datamodel-code-generator can be used as a module to get the dictionary to python-code-string (see https://koxudaxi.github.io/datamodel-code-generator/using_as_module/).

Here is there relevant example code (to avoid problems with their pages changing)

from datamodel_code_generator import DataModelType, PythonVersion
from datamodel_code_generator.model import get_data_model_types
from datamodel_code_generator.parser.jsonschema import JsonSchemaParser

json_schema: str = """{
    "type": "object",
    "properties": {
        "number": {"type": "number"},
        "street_name": {"type": "string"},
        "street_type": {"type": "string",
                        "enum": ["Street", "Avenue", "Boulevard"]
                        }
    }
}"""


data_model_types = get_data_model_types(
    DataModelType.PydanticV2BaseModel,
    target_python_version=PythonVersion.PY_311
)
parser = JsonSchemaParser(
   json_schema,
   data_model_type=data_model_types.data_model,
   data_model_root_type=data_model_types.root_model,
   data_model_field_type=data_model_types.field_model,
   data_type_manager_type=data_model_types.data_type_manager,
   dump_resolve_reference_action=data_model_types.dump_resolve_reference_action,
                       )
result = parser.parse()

Then, assuming that there isn't a way to send in a schema that would cause their parser to generate malicious code, you just need to run

exec(result)

EDIT: Here is a worked out example using the example code. I get an error with your schema, which I have not tried to debug.

>>> from datamodel_code_generator import DataModelType, PythonVersion
>>> from datamodel_code_generator.model import get_data_model_types
>>> from datamodel_code_generator.parser.jsonschema import JsonSchemaParser
>>> json_schema: str = """{
...     "type": "object",
...     "properties": {
...         "number": {"type": "number"},
...         "street_name": {"type": "string"},
...         "street_type": {"type": "string",
...                         "enum": ["Street", "Avenue", "Boulevard"]
...                         }
...     }
... }"""
>>> parser = JsonSchemaParser(
...    json_schema,
...    data_model_type=data_model_types.data_model,
...    data_model_root_type=data_model_types.root_model,
...    data_model_field_type=data_model_types.field_model,
...    data_type_manager_type=data_model_types.data_type_manager,
...    dump_resolve_reference_action=data_model_types.dump_resolve_reference_action,
... )
>>> result = parser.parse()
>>> print(result)
from __future__ import annotations

from enum import Enum
from typing import Optional

from pydantic import BaseModel


class StreetType(Enum):
    Street = 'Street'
    Avenue = 'Avenue'
    Boulevard = 'Boulevard'


class Model(BaseModel):
    number: Optional[float] = None
    street_name: Optional[str] = None
    street_type: Optional[StreetType] = None

>>> exec(result)
>>> m = Model(**{'number': 1, 'street_name': 'Main', 'street_type': 'Street'})
>>> print(m)
number=1.0 street_name='Main' street_type=<StreetType.Street: 'Street'>
>>>