#0: Fix typos in ttnn docs

tech_reports/Programming Mesh of Devices/Programming Mesh of Devices with TT-NN.md

@@ -41,7 +41,7 @@ TT-NN library natively supports multi-device operations, enabling users to scale
 
 - **MeshDevice**: This "virtual device" abstraction defines a logical 2-D mesh of connected physical devices. Operations that "run on device" are distributed through SPMD across all devices captured in the mesh.
 
-- **Input Data Distribution**: Defines how input data resident in host-memory is distributed to DeviceMesh on-device memory. When operations are distributed to MeshDevice, the operation within a single-device scope works on its local input data.
+- **Input Data Distribution**: Defines how input data resident in host-memory is distributed to MeshDevice on-device memory. When operations are distributed to MeshDevice, the operation within a single-device scope works on its local input data.
 
 - **Tensor**: Defines a N-dimensional matrix containing elements of a single data type. In a MeshDevice context, a Tensor, or colloquially referred to as MeshTensor, represents a collection of tensor shards distributed across devices in a 2D Mesh.
 
@@ -138,7 +138,7 @@ torch_tensor[..., 0:32] = 1.0
 torch_tensor[..., 32:64] = 2.0
 
 # Convert to ttnn.Tensor; MeshTensor holds buffers to two shards in host-memory
-mesh_tensor: ttnn.Tensor = ttnn.from_torch(
+mesh_tensor = ttnn.from_torch(
     torch_tensor,
     mesh_mapper=ttnn.ShardTensorToMesh(mesh_device, dim=3),
     layout=ttnn.TILE_LAYOUT,
@@ -165,7 +165,7 @@ ttnn.Tensor([[[[ 2.00000,  2.00000,  ...,  2.00000,  2.00000],
 Let's now transfer to device:
 
 ```py
-> mesh_tensor = ttnn.to_device(mesh_tensor, device_mesh)
+> mesh_tensor = ttnn.to_device(mesh_tensor, mesh_device)
 > mesh_tensor
 
 device_id:0
@@ -194,7 +194,7 @@ We can also visualize this tensor distributed across our MeshDevice. The visuali
 ttnn.visualize_mesh_device(mesh_device, tensor=mesh_tensor)
 
 >
-                  DeviceMesh(rows=1, cols=2):
+                  MeshDevice(rows=1, cols=2):
 ┌──────────────────────────────┬──────────────────────────────┐
 │         Dev. ID: 0           │         Dev. ID: 1           │
 │            (0, 0)            │            (0, 1)            │

ttnn/cpp/ttnn/operations/data_movement/concat/concat_pybind.hpp

@@ -18,7 +18,7 @@ void bind_concat(py::module& module) {
     const auto doc = R"doc(
 
 Args:
-    input_tensor (ttnn.Tensor): the input tensor.
+    input_tensor (List of ttnn.Tensor): the input tensors.
     dim (number): the concatenating dimension.
 
 Keyword Args:
@@ -32,13 +32,11 @@ Keyword Args:
 
 Example:
 
-    >>> tensor = ttnn.concat(ttnn.from_torch(torch.zeros((1, 1, 64, 32), ttnn.from_torch(torch.zeros((1, 1, 64, 32), dim=3)), device)
-
     >>> tensor1 = ttnn.from_torch(torch.zeros((1, 1, 64, 32), dtype=torch.bfloat16), device=device)
     >>> tensor2 = ttnn.from_torch(torch.zeros((1, 1, 64, 32), dtype=torch.bfloat16), device=device)
-    >>> output = ttnn.concat([tensor1, tensor2], dim=4)
+    >>> output = ttnn.concat([tensor1, tensor2], dim=3)
     >>> print(output.shape)
-    [1, 1, 32, 64]
+    [1, 1, 64, 64]
 
     )doc";
 

ttnn/cpp/ttnn/operations/data_movement/non_zero_indices/non_zero_indices_pybind.cpp

@@ -27,7 +27,7 @@ void bind_non_zero(py::module& module) {
                 queue_id (int, optional): command queue id. Defaults to `0`.
 
             Returns:
-                List of ttnn.Tensor: the output tensor.
+                List of ttnn.Tensor: the output tensors.
 
             Example: