Refactor dtype conversion in convert_to_tensor function and fix to_ml…

…x_dtype

keras/src/backend/mlx/core.py

@@ -59,32 +59,35 @@ def __array__(self, dtype=None):
 def convert_to_tensor(x, dtype=None, sparse=None):
     if sparse:
         raise ValueError("`sparse=True` is not supported with mlx backend")
-    # Convert the dtype to mlx.Dtype early to avoid comparison issues later.
+    # Convert the dtype to a compatible numpy dtype if necessary.
     mlx_dtype = to_mlx_dtype(dtype) if dtype is not None else None
+    np_dtype = (
+        np.dtype(mlx_dtype) if mlx_dtype else None
+    )  # Ensure dtype is compatible with numpy
 
     if is_tensor(x):
         # Only cast if necessary to avoid redundant operations
         if dtype and x.dtype != mlx_dtype:
-            x = x.astype(mlx_dtype)
+            x = x.astype(np_dtype)
         return x
 
     if isinstance(x, Variable):
         # Adjust Variable objects if dtype is different
         if dtype and standardize_dtype(dtype) != x.dtype:
-            x = x.value.astype(mlx_dtype)
+            x = x.value.astype(np_dtype)
         return x.value
 
     if isinstance(x, np.ndarray):
         # Ensure dtype consistency for numpy arrays
-        if dtype and x.dtype != np.dtype(mlx_dtype):
-            x = x.astype(mlx_dtype)
-        return mx.array(x, dtype=mlx_dtype)
+        if dtype and x.dtype != np_dtype:
+            x = x.astype(np_dtype)
+        return mx.array(x, dtype=np_dtype)
 
     # Handle conversion for lists to ensure dtype uniformity
     if isinstance(x, list):
-        return mx.array(x, dtype=mlx_dtype)
+        return mx.array(x, dtype=np_dtype)
 
-    return mx.array(x, dtype=mlx_dtype)
+    return mx.array(x, dtype=np_dtype)
 
 
 def convert_to_tensors(*xs):