docstr: Checks that input is a dataframe, checks that variables in the dictionary
  entered by the user are of type numerical.
  
  Parameters
  ----------
  X : Pandas DataFrame
  
  user_dict_ : Dictionary. Default = None
      Any dictionary allowed by the transformer and entered by user.
  
  Raises
  ------
  TypeError
      If the input is not a Pandas DataFrame or a numpy array
      If any of the variables in the dictionary are not numerical
  ValueError
      If there are no numerical variables in the df or the df is empty
      If the variable(s) contain null values
  
  Returns
  -------
  X : Pandas DataFrame
      The same dataframe entered as parameter
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code: def _fit_from_dict(
      self, X: pd.DataFrame, user_dict_: Dict
  ) -> pd.DataFrame:
      """
      Checks that input is a dataframe, checks that variables in the dictionary
      entered by the user are of type numerical.
  
      Parameters
      ----------
      X : Pandas DataFrame
  
      user_dict_ : Dictionary. Default = None
          Any dictionary allowed by the transformer and entered by user.
  
      Raises
      ------
      TypeError
          If the input is not a Pandas DataFrame or a numpy array
          If any of the variables in the dictionary are not numerical
      ValueError
          If there are no numerical variables in the df or the df is empty
          If the variable(s) contain null values
  
      Returns
      -------
      X : Pandas DataFrame
          The same dataframe entered as parameter
      """
      # check input dataframe
      X = check_X(X)
  
      # find or check for numerical variables
      variables = [x for x in user_dict_.keys()]
      self.variables_ = _find_or_check_numerical_variables(X, variables)
  
      # check if dataset contains na or inf
      _check_contains_na(X, self.variables_)
      _check_contains_inf(X, self.variables_)
  
      # save input features
      self.feature_names_in_ = X.columns.tolist()
  
      # save train set shape
      self.n_features_in_ = X.shape[1]
  
      return X
  
file_path: feature_engine/feature_engine/_base_transformers/mixins.py
imports: from typing import Dict, List, Union
  import pandas
  from numpy import ndarray
  from numpy.typing import ArrayLike
  from sklearn.utils.validation import check_is_fitted
  from feature_engine._variable_handling.variable_type_selection import _find_or_check_numerical_variables
  from feature_engine.dataframe_checks import _check_contains_inf, _check_contains_na, check_X
label_desc: Use image node_id 1 for calling the FitFromDictMixin obj's underlying member method code with example usage: obj._fit_from_dict(X, user_dict_) and returns: X