Fixes monospace formatting for layer subclassing docs (#2060)

Author: JyotinderSingh

guides/ipynb/making_new_layers_and_models_via_subclassing.ipynb

@@ -97,8 +97,7 @@
     "        self.b = self.add_weight(shape=(units,), initializer=\"zeros\", trainable=True)\n",
     "\n",
     "    def call(self, inputs):\n",
-    "        return ops.matmul(inputs, self.w) + self.b\n",
-    ""
+    "        return ops.matmul(inputs, self.w) + self.b\n"
    ]
   },
   {
@@ -246,8 +245,7 @@
     "        self.b = self.add_weight(shape=(units,), initializer=\"zeros\", trainable=True)\n",
     "\n",
     "    def call(self, inputs):\n",
-    "        return ops.matmul(inputs, self.w) + self.b\n",
-    ""
+    "        return ops.matmul(inputs, self.w) + self.b\n"
    ]
   },
   {
@@ -289,8 +287,7 @@
     "        )\n",
     "\n",
     "    def call(self, inputs):\n",
-    "        return ops.matmul(inputs, self.w) + self.b\n",
-    ""
+    "        return ops.matmul(inputs, self.w) + self.b\n"
    ]
   },
   {
@@ -390,7 +387,7 @@
     "The `keras.ops` namespace gives you access to:\n",
     "\n",
     "- The NumPy API, e.g. `ops.matmul`, `ops.sum`, `ops.reshape`, `ops.stack`, etc.\n",
-    "- Neural networks-specific APIs such as `ops.softmax`, `ops`.conv`, `ops.binary_crossentropy`, `ops.relu`, etc.\n",
+    "- Neural networks-specific APIs such as `ops.softmax`, `ops.conv`, `ops.binary_crossentropy`, `ops.relu`, etc.\n",
     "\n",
     "You can also use backend-native APIs in your layers (such as `tf.nn` functions),\n",
     "but if you do this, then your layer will only be usable with the backend in question.\n",
@@ -465,8 +462,7 @@
     "\n",
     "    def call(self, inputs):\n",
     "        self.add_loss(self.rate * ops.mean(inputs))\n",
-    "        return inputs\n",
-    ""
+    "        return inputs\n"
    ]
   },
   {
@@ -742,8 +738,7 @@
     "            return keras.random.dropout(\n",
     "                inputs, rate=self.rate, seed=self.seed_generator\n",
     "            )\n",
-    "        return inputs\n",
-    ""
+    "        return inputs\n"
    ]
   },
   {
@@ -947,8 +942,7 @@
     "            z_log_var - ops.square(z_mean) - ops.exp(z_log_var) + 1\n",
     "        )\n",
     "        self.add_loss(kl_loss)\n",
-    "        return reconstructed\n",
-    ""
+    "        return reconstructed\n"
    ]
   },
   {
@@ -1010,4 +1004,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}

guides/making_new_layers_and_models_via_subclassing.py

@@ -227,7 +227,7 @@ def call(self, inputs):
 The `keras.ops` namespace gives you access to:
 
 - The NumPy API, e.g. `ops.matmul`, `ops.sum`, `ops.reshape`, `ops.stack`, etc.
-- Neural networks-specific APIs such as `ops.softmax`, `ops`.conv`, `ops.binary_crossentropy`, `ops.relu`, etc.
+- Neural networks-specific APIs such as `ops.softmax`, `ops.conv`, `ops.binary_crossentropy`, `ops.relu`, etc.
 
 You can also use backend-native APIs in your layers (such as `tf.nn` functions),
 but if you do this, then your layer will only be usable with the backend in question.

guides/md/making_new_layers_and_models_via_subclassing.md

@@ -272,7 +272,7 @@ All layers you've seen so far in this guide work with all Keras backends.
 The `keras.ops` namespace gives you access to:
 
 - The NumPy API, e.g. `ops.matmul`, `ops.sum`, `ops.reshape`, `ops.stack`, etc.
-- Neural networks-specific APIs such as `ops.softmax`, `ops`.conv`, `ops.binary_crossentropy`, `ops.relu`, etc.
+- Neural networks-specific APIs such as `ops.softmax`, `ops.conv`, `ops.binary_crossentropy`, `ops.relu`, etc.
 
 You can also use backend-native APIs in your layers (such as `tf.nn` functions),
 but if you do this, then your layer will only be usable with the backend in question.