Running mypy on script with pd.pivot_table gets exponentially slower with more columns

Bug Report

When running mypy on a script containing pd.pivot_table, the amount of time for mypy to finish grows exponentially with the number of columns selected and aggregated in the table.

To Reproduce

from string import ascii_letters

import numpy as np
import pandas as pd

df = pd.DataFrame({letter: [1, 2, 3] for letter in ascii_letters[:14]})

df2 = pd.pivot_table(
    df,
    values=["a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n"],
    columns=["a"],
    aggfunc={
        "a": np.sum,
        "b": np.sum,
        "c": np.sum,
        "d": np.sum,
        "e": np.sum,
        "f": np.sum,
        "g": np.sum,
        "h": np.sum,
        "i": np.sum,
        "j": np.sum,
        "k": np.sum,
        "l": np.sum,
        "m": np.sum,
        "n": np.sum,
    },
)

Expected Behavior

It should finish in <1s, like it does if there are fewer columns

Actual Behavior

Takes 200 seconds to run mypy on this script

Your Environment

• Mypy version used: 1.7.0
• Mypy command-line flags: None
• Mypy configuration options from mypy.ini (and other config files): None
• Python version used: 3.8.15

Start by running the provided Python reproducer with mypy 1.7.0 and compare runtime as the selected columns increase. Profile the type-checking path to identify the source of the growth; done means the reproducer completes in under one second with the larger column set without changing its behavior.