### `tf.histogram_fixed_width(values, value_range, nbins=100, dtype=tf.int32, name=None)` {#histogram_fixed_width}

Return histogram of values.

Given the tensor `values`, this operation returns a rank 1 histogram counting
the number of entries in `values` that fell into every bin.  The bins are
equal width and determined by the arguments `value_range` and `nbins`.

##### Args:


*  <b>`values`</b>: Numeric `Tensor`.
*  <b>`value_range`</b>: Shape [2] `Tensor`.  new_values <= value_range[0] will be
    mapped to hist[0], values >= value_range[1] will be mapped to hist[-1].
    Must be same dtype as new_values.
*  <b>`nbins`</b>: Scalar `int32 Tensor`.  Number of histogram bins.
*  <b>`dtype`</b>: dtype for returned histogram.
*  <b>`name`</b>: A name for this operation (defaults to 'histogram_fixed_width').

##### Returns:

  A 1-D `Tensor` holding histogram of values.


*  <b>`Examples`</b>: 

```python
# Bins will be:  (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
nbins = 5
value_range = [0.0, 5.0]
new_values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15]

with tf.default_session() as sess:
  hist = tf.histogram_fixed_width(new_values, value_range, nbins=5)
  variables.global_variables_initializer().run()
  sess.run(hist) => [2, 1, 1, 0, 2]
```

