*Last updated: 2023-03-16.*
# tf_quant_finance.math.pde.grids.uniform_grid
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Creates a grid spec for a uniform grid.
```python
tf_quant_finance.math.pde.grids.uniform_grid(
minimums, maximums, sizes, dtype=None, validate_args=False, name=None
)
```
A uniform grid is characterized by having a constant gap between neighboring
points along each axis.
Note that the shape of all three parameters must be fully defined and equal
to each other. The shape is used to determine the dimension of the grid.
#### Args:
* `minimums`: Real `Tensor` of rank 1 containing the lower end points of the
grid. Must have the same shape as those of `maximums` and `sizes` args.
* `maximums`: `Tensor` of the same dtype and shape as `minimums`. The upper
endpoints of the grid.
* `sizes`: Integer `Tensor` of the same shape as `minimums`. The size of the
grid in each axis. Each entry must be greater than or equal to 2 (i.e. the
sizes include the end points). For example, if minimums = [0.] and
maximums = [1.] and sizes = [3], the grid will have three points at [0.0,
0.5, 1.0].
* `dtype`: Optional tf.dtype. The default dtype to use for the grid.
* `validate_args`: Python boolean indicating whether to validate the supplied
arguments. The validation checks performed are (a) `maximums` > `minimums`
(b) `sizes` >= 2.
* `name`: Python str. The name prefixed to the ops created by this function. If
not supplied, the default name 'uniform_grid_spec' is used.
#### Returns:
The grid locations as projected along each axis. One `Tensor` of shape
`[..., n]`, where `n` is the number of points along that axis. The first
dimensions are the batch shape. The grid itself can be seen as a cartesian
product of the locations array.
#### Raises:
ValueError if the shape of maximums, minimums and sizes are not fully
defined or they are not identical to each other or they are not rank 1.