code: def type_validation(**kwargs: Any) -> None:
      """
      Perform generic type validations on input variables.
  
      This function performs various type validations on a set of input variables. It helps to ensure that the input
      values conform to the expected types and conditions, raising a TypeError with a descriptive error message
      if any type validation fails and a ValueError if a numpy.array or pd.Series/DataFrame is empty.
  
      :param kwargs: Arbitrary keyword arguments representing the input variables to be checked.
  
      Raises:
          ``TypeError``:
              If any of the type validations fail, a TypeError is raised with a descriptive error message
              indicating the expected type and conditions for each variable.
          ``ValueError``:
              If any of the value validations fail, a ValueError is raised with a descriptive error message
              indicating the expected conditions for each variable.
  
      Example usage:
  
      .. code-block:: python
  
          type_validation(
              data=pd.DataFrame([1., 2.]),
              names=["name1", "name2"],
              start_date="2023-08-01",
              freq=10,
          )
      """
  
      for arg_name, arg_values in kwargs.items():
          if (
              arg_name not in type_dict
              and arg_name not in type_callable_dict
          ):
              raise ValueError(
                  f"Error: '{arg_name}' is not a valid argument. "
                  f"Please only use argument names defined in `type_dict` or `type_callable_dict`."
              )
  
          # Some arguments are allowed to be None, so skip them
          if arg_values is None:
              continue
  
          # Perform the type validation
          if arg_name == "fun":
              _check_callable_type(arg_name, arg_values)
          else:
              expected_type, element_type = type_dict[arg_name]
              # Validation of type
              _check_type(
                  arg_name, arg_values, expected_type, element_type
              )
              # Check for empty list/array/series/dataframe
              _check_empty_data(arg_name, arg_values)
  
file_path: FinQuant/finquant/type_utilities.py
imports: import datetime
  from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
  import numpy
  import pandas
label_desc: Use image node_id 4 for calling a global function with example usage: type_validation() without return types