Add 1D indices support for embedding (#15726)

### Ticket
Link to Github Issue

### Problem description
Trying to use embedding with no batches results in an invalid reshape

### What's changed
added conditional reshaping/batch size instantiation for whether the
indices tensor is 1 or 2 dims.

### Checklist
- [ ] Post commit CI passes:
#15726 (comment)

ttnn/cpp/ttnn/operations/embedding/embedding.cpp

@@ -33,6 +33,10 @@ ttnn::Tensor EmbeddingOperation::invoke(
     // Issue #: 14915
     TT_FATAL(input_tensor_arg.get_layout() == ttnn::ROW_MAJOR_LAYOUT, "Indices tensor must be in row major layout.");
 
+    if (mutable_input_tensor.get_layout() == ttnn::TILE_LAYOUT) {
+        mutable_input_tensor = ttnn::to_layout(
+            mutable_input_tensor, ttnn::ROW_MAJOR_LAYOUT, std::nullopt, std::nullopt, mutable_input_tensor.device());
+    }
     if (mutable_weight.get_layout() == ttnn::TILE_LAYOUT) {
         mutable_weight = ttnn::to_layout(
             mutable_weight, ttnn::ROW_MAJOR_LAYOUT, std::nullopt, std::nullopt, mutable_weight.device());
@@ -41,7 +45,8 @@ ttnn::Tensor EmbeddingOperation::invoke(
     auto padded_hidden_embedding_dim = mutable_weight.get_shape().with_tile_padding()[-1];
     auto weight = ttnn::unsqueeze_to_4D(mutable_weight);
 
-    auto batch_size = mutable_input_tensor.get_shape()[0];
+    // If indices tensor is 1 dimensional, batch size is 1
+    auto batch_size = (mutable_input_tensor.get_shape().rank() == 1) ? 1 : mutable_input_tensor.get_shape()[0];
     auto sentence_size = mutable_input_tensor.get_shape()[-1];
     auto input_tensor =
         ttnn::reshape(mutable_input_tensor, ttnn::Shape{std::array<uint32_t, 4>{batch_size, 1, 1, sentence_size}});
@@ -67,8 +72,14 @@ ttnn::Tensor EmbeddingOperation::invoke(
                               .output_dtype = dtype.value_or(weight.get_dtype())},
                           {input_tensor, weight})
                           .at(0);
-    embeddings = ttnn::reshape(
-        embeddings, ttnn::Shape{std::array<uint32_t, 3>{batch_size, sentence_size, hidden_embedding_dim}});
+    // Don't include batch_size if there was none
+    if (input_tensor_arg.get_shape().rank() == 1) {
+        embeddings =
+            ttnn::reshape(embeddings, ttnn::Shape{std::array<uint32_t, 2>{sentence_size, hidden_embedding_dim}});
+    } else {
+        embeddings = ttnn::reshape(
+            embeddings, ttnn::Shape{std::array<uint32_t, 3>{batch_size, sentence_size, hidden_embedding_dim}});
+    }
     embeddings = ttnn::to_layout(
         embeddings, layout.value_or(weight_arg.get_layout()), std::nullopt, std::nullopt, (Device*)nullptr);
     return embeddings;