plt.hist() fails with TensorFlow Numpy emulation

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### Bug report

**Bug summary**

Generating `np.random.randn(1000)` values, visualizing them with `plt.hist()`. Works fine with Numpy.

When I replace Numpy with tensorflow.experimental.numpy, Matplotlib 3.3.4 fails to display the histogram correctly. Matplotlib 3.2.2 works fine.

**Code for reproduction**

```python
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import tensorflow.experimental.numpy as tnp

# bad image
labels1 = 15 + 2 * tnp.random.randn(1000)
_ = plt.hist(labels1)

# good image
labels2 = 15 + 2 * np.random.randn(1000)
_ = plt.hist(labels2)
```

**Actual outcome**


**Expected outcome**


**Matplotlib version**

* Operating system: Windows 10
* Matplotlib version (`import matplotlib; print(matplotlib.__version__)`): 3.3.4
* Matplotlib backend (`print(matplotlib.get_backend())`): module://ipykernel.pylab.backend_inline
* Python version: 3.8.7
* Jupyter version (if applicable): see below
* Other libraries: see below

TensorFlow 2.4.1

```
jupyter --version
jupyter core : 4.7.0
jupyter-notebook : 6.1.6
qtconsole : 5.0.1
ipython : 7.20.0
ipykernel : 5.4.2
jupyter client : 6.1.7
jupyter lab : not installed
nbconvert : 6.0.7
ipywidgets : 7.6.3
nbformat : 5.0.8
traitlets : 5.0.5
```

Python installed from python.org as an exe installer. Everything else is `pip install --user`

Bug opened with TensorFlow on this same issue:

https://github.com/tensorflow/tensorflow/issues/46274

The issue names the plt.hist() entry point but no repository files or tests. First run the supplied reproduction with TensorFlow 2.4.1 and Matplotlib 3.3.4, then compare how the histogram input is handled against Matplotlib 3.2.2. Done means TensorFlow NumPy arrays produce the same correctly scaled histogram as regular NumPy arrays, with a regression test.

Written by the indexing model from the issue text.