if (chunk > SSIZE_MAX) chunk = SSIZE_MAX;

    rc = sendfile(dst_fd, src_fd, &offset, static_cast<size_t>(chunk));
    if (rc < 0) return -1;
    bytes_transferred += rc;
  }

  return bytes_transferred;
}

    }
    int retvals = outputs.size() - null_indices.size();
    std::vector<AbstractTensorHandle*> fn_outputs(retvals);
    TF_RETURN_IF_ERROR(fn_op->Execute(
        absl::Span<AbstractTensorHandle*>(fn_outputs.data(), fn_outputs.size()),
        &retvals));
    int skipped_indices = 0;
    for (int i = 0; i < outputs.size(); i++) {
      if (!null_indices.contains(i)) {

        // Empty the buffer to avoid caching bad reads.
        gcs_file->buffer.resize(0);
        return -1;
      }
      size_t remaining_copy =
          (std::min)(n - copy_size, gcs_file->buffer.size());
      memcpy(buffer + copy_size, gcs_file->buffer.data(), remaining_copy);
      copy_size += remaining_copy;
    }
    if (copy_size < n) {
      // Forget the end-of-file flag to allow for clients that poll on the

    *   Added gradient for `SparseToDense` op.
    *   Expose a flag that allows the number of threads to vary across Python
        benchmarks.
    *   `image.resize` in 2.0 now supports gradients for the new resize kernels.
    *   `image.resize` now considers proper pixel centers and has new kernels
        (incl. anti-aliasing).
    *   Renamed `tf.image` functions to remove duplicate "image" where it is
        redundant.

  }

  void CompareInt32Tensor(const std::vector<int32_t>& expected, TF_Tensor* t) {
    int32_t* data = static_cast<int32_t*>(TF_TensorData(t));
    size_t size = TF_TensorByteSize(t);
    ASSERT_EQ(expected.size() * sizeof(int32_t), size);
    for (int i = 0; i < expected.size(); ++i) {
      ASSERT_EQ(expected[i], data[i]) << "Different data at index " << i;
    }
  }

  int filesystem_ops_abi;
  int filesystem_ops_api;
  size_t filesystem_ops_size;
  TF_FilesystemOps* filesystem_ops;
  int random_access_file_ops_abi;
  int random_access_file_ops_api;
  size_t random_access_file_ops_size;
  TF_RandomAccessFileOps* random_access_file_ops;
  int writable_file_ops_abi;
  int writable_file_ops_api;
  size_t writable_file_ops_size;
  TF_WritableFileOps* writable_file_ops;

#include "tensorflow/c/tf_status.h"

// Implementation of a filesystem for POSIX environments.
// This filesystem will support `file://` and empty (local) URI schemes.

static void* plugin_memory_allocate(size_t size) { return calloc(1, size); }
static void plugin_memory_free(void* ptr) { free(ptr); }

// SECTION 1. Implementation for `TF_RandomAccessFile`
// ----------------------------------------------------------------------------

            size = vspace.NumElements(unaggregated_grads[0]);
            gradients_size.emplace(id, size);
          } else {
            size = size_it->second;
          }
          if (unaggregated_grads.size() * size * 4 > kMinAggregateBytes) {
            Gradient* grad = vspace.AggregateGradients(unaggregated_grads);
            unaggregated_grads.clear();
            unaggregated_grads.push_back(grad);
          }
        }

# MACOS ARM64 WHEEL

}

@test "Wheel conforms to upstream size limitations" {
    WHEEL_MEGABYTES=$(stat --format %s "$TF_WHEEL" | awk '{print int($1/(1024*1024))}')
    # Googlers: search for "test_tf_whl_size"
    case "$TF_WHEEL" in
        # CPU:
        *cpu*manylinux*) LARGEST_OK_SIZE=220 ;;
        # GPU:
        *manylinux*)     LARGEST_OK_SIZE=580 ;;
        # Unknown:
        *)