diff --git a/tensorflow/compiler/mlir/lite/ir/tfl_ops.td b/tensorflow/compiler/mlir/lite/ir/tfl_ops.td
index e2366d8cd80..b9972cebdce 100644
--- a/tensorflow/compiler/mlir/lite/ir/tfl_ops.td
+++ b/tensorflow/compiler/mlir/lite/ir/tfl_ops.td
@@ -1965,6 +1965,25 @@ def TFL_StridedSliceOp: TFL_Op<"strided_slice",
   let hasOptions = 1;
 }
 
+def TFL_CastOp : TFL_Op<"cast", [NoSideEffect, SameOperandsAndResultShape]> {
+  let summary = "Cast operator";
+
+  let description = [{
+    Casts input from input type to output type.
+  }];
+
+  // TODO(b/135538711): Add complex types here.
+  let arguments = (ins
+    TensorOf<[F32, I1, I32, I64]>:$input
+  );
+
+  let results = (outs TensorOf<[F32, I1, I32, I64]>:$output);
+
+  // TFLite's cast op does not utilize CastOptions, instead derives types
+  // from the TfLiteTensors.
+  let hasOptions = 0;
+}
+
 
 def TFL_MirrorPadOp: TFL_Op<"mirror_pad", [
                      NoSideEffect, TFL_OperandHasRank<1, 2>]> {
diff --git a/tensorflow/compiler/mlir/lite/tests/legalize-tf.mlir b/tensorflow/compiler/mlir/lite/tests/legalize-tf.mlir
index f771e0b7504..6b29869dde5 100644
--- a/tensorflow/compiler/mlir/lite/tests/legalize-tf.mlir
+++ b/tensorflow/compiler/mlir/lite/tests/legalize-tf.mlir
@@ -857,3 +857,11 @@ func @Tanh(%arg0: tensor<1xf32>) -> tensor<1xf32> {
 // CHECK-LABEL: Tanh
 // CHECK:  %0 = "tfl.tanh"(%arg0) : (tensor<1xf32>) -> tensor<1xf32>
 }
+
+func @cast(%arg0: tensor<1x2x2x5xi32>) -> tensor<1x2x2x5xf32> {
+  %0 = "tf.Cast"(%arg0) : (tensor<1x2x2x5xi32>) -> tensor<1x2x2x5xf32>
+  return %0 : tensor<1x2x2x5xf32>
+
+  // CHECK-LABEL: cast
+  // CHECK: "tfl.cast"(%arg0) : (tensor<1x2x2x5xi32>) -> tensor<1x2x2x5xf32>
+}
diff --git a/tensorflow/compiler/mlir/lite/transforms/legalize_patterns.td b/tensorflow/compiler/mlir/lite/transforms/legalize_patterns.td
index 0fac8a09f86..d4866a4740e 100644
--- a/tensorflow/compiler/mlir/lite/transforms/legalize_patterns.td
+++ b/tensorflow/compiler/mlir/lite/transforms/legalize_patterns.td
@@ -235,6 +235,8 @@ def : Pat<(TF_MaxOp $arg0, $arg1, BoolAttr:$arg2), (TFL_ReduceMaxOp $arg0, $arg1
 
 def : Pat<(TF_ProdOp $arg0, $arg1, BoolAttr:$arg2), (TFL_ReduceProdOp $arg0, $arg1, $arg2)>;
 
+def : Pat<(TF_CastOp $arg0, BoolAttr:$arg1), (TFL_CastOp $arg0)>;
+
 def : Pat<(TF_BatchToSpaceNDOp $input, $block_shape, $crops), (TFL_BatchToSpaceNdOp $input, $block_shape, $crops)>;
 
 def : Pat<(TF_SpaceToBatchNDOp $input, $block_shape, $paddings), (TFL_SpaceToBatchNdOp $input, $block_shape, $paddings)>;
diff --git a/tensorflow/lite/build_def.bzl b/tensorflow/lite/build_def.bzl
index 9bdf0547d45..3c9337121f6 100644
--- a/tensorflow/lite/build_def.bzl
+++ b/tensorflow/lite/build_def.bzl
@@ -235,6 +235,7 @@ def generated_test_models():
         "arg_min_max",
         "avg_pool",
         "batch_to_space_nd",
+        "cast",
         "ceil",
         "concat",
         "constant",
diff --git a/tensorflow/lite/testing/generate_examples_lib.py b/tensorflow/lite/testing/generate_examples_lib.py
index 431a9aa541b..31c8f94a075 100644
--- a/tensorflow/lite/testing/generate_examples_lib.py
+++ b/tensorflow/lite/testing/generate_examples_lib.py
@@ -3856,6 +3856,33 @@ def make_zeros_like_tests(options):
   make_zip_of_tests(options, test_parameters, build_graph, build_inputs)
 
 
+@register_make_test_function()
+def make_cast_tests(options):
+  """Generate examples for cast."""
+  test_parameters = [{
+      "input_dtype": [tf.int32],
+      "output_dtype": [tf.float32],
+      "input_shape": [[], [1], [1, 2], [5, 6, 7, 8], [3, 4, 5, 6]],
+  }]
+
+  def build_graph(parameters):
+    """Build the cast testing graph."""
+    input_value = tf.placeholder(
+        dtype=parameters["input_dtype"],
+        name="input",
+        shape=parameters["input_shape"])
+    out = tf.cast(input_value, parameters["output_dtype"])
+    return [input_value], [out]
+
+  def build_inputs(parameters, sess, inputs, outputs):
+    input_value = create_tensor_data(parameters["input_dtype"],
+                                     parameters["input_shape"])
+    return [input_value], sess.run(
+        outputs, feed_dict=dict(zip(inputs, [input_value])))
+
+  make_zip_of_tests(options, test_parameters, build_graph, build_inputs)
+
+
 def _make_elementwise_tests(op):
   """Make a set of tests to do element-wise operations."""