
At this time we only provide Linux-64 binaries which have been tested on Ubuntu 16.04 and CentOS7.

TC depends on heavyweight C++ projects such as [Halide](http://halide-lang.org/), [Tapir-LLVM](https://github.com/wsmoses/Tapir-LLVM) and [ISL](http://isl.gforge.inria.fr/). Hence, we rely on Anaconda to distribute these dependencies reliably. For the same reason, TC is not available via PyPI.

#### 2. Import the python package

```python
import tensor_comprehensions as tc

3. Define the TC expression and create a python function
lang = """
def fcrelu(float(B,M) I, float(N,M) W1, float(N) B1) -> (O1) {
    O1(b, n) +=! I(b, m) * W1(n, m)
    O1(b, n) = O1(b, n) + B1(n)
    O1(b, n) = fmax(O1(b, n), 0)
}
"""
fcrelu = tc.define(lang, name="fcrelu")

This fcrelu function takes PyTorch Tensors as input and returns a PyTorch Tensor. It takes input I, weight W1, bias B1 and returns output O1.
4. Let's create some dummy input tensors
```python
B, M, N = 100, 128, 100